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Abbas Mammadov (University of Oxford).
Tuesday 24 March 2026, 15:00-16:30
Contact: Tomas Van Pottelbergh
0 upcoming talks View 1301 archived talks
Machine Learning Reading Group @ CUED
From Stochastic Interpolants to Flow Maps: Foundations, Fast Generation, and Applications to Weather & Climate Modeling
Machine Learning Reading Group @ CUED
Evolution Strategies at the Hyperscale
Machine Learning Reading Group @ CUED
Evolution Strategies at the Hyperscale
Machine Learning Reading Group @ CUED
Machine Learning on Tabular Data
Machine Learning Reading Group @ CUED
Rethinking aleatoric and epistemic uncertainty
Machine Learning Reading Group @ CUED
How does gradient descent work?
Machine Learning Reading Group @ CUED
Variational Inference: An Algorithm-Centric Perspective
Machine Learning Reading Group @ CUED
Neural Operators for Scientific Simulation
Machine Learning Reading Group @ CUED
A Tutorial on Algorithmic Information Theory in Modern ML
Machine Learning Reading Group @ CUED
Diffusion Language Models
Machine Learning Reading Group @ CUED
An Introduction to Stochastic Interpolants
Cambridge Statistics Clinic
Statistics Clinic Summer 2025 II
Machine Learning Reading Group @ CUED
The structure of curvature in neural networks
⚠️ Teams link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Variational Uncertainty Decomposition for In-Context Learning
Machine Learning Reading Group @ CUED
Variational Uncertainty Decomposition for In-Context Learning
Machine Learning Reading Group @ CUED
Vision-language models (VLMs)
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Machine Learning Reading Group @ CUED
Vision-language models (VLMs)
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Machine Learning Reading Group @ CUED
Conditional Expectation and Machine Learning
Machine Learning Reading Group @ CUED
Evaluating and Regulating Foundation Models
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Machine Learning Reading Group @ CUED
Out-of-context reasoning/learning in LLMs and its safety implications
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Machine Learning Reading Group @ CUED
Learning to See the World in 3D
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Machine Learning Reading Group @ CUED
An Introduction to In-Context Learning
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Machine Learning Reading Group @ CUED
ML for Medium-Range Weather Forecasting
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Machine Learning Reading Group @ CUED
Influence Functions
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Machine Learning Reading Group @ CUED
Sampling with diffusion models
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Machine Learning Reading Group @ CUED
Geometric Deep Learning for Structure-Based Drug Design
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Machine Learning Reading Group @ CUED
Diffusion Models Beyond Mean Prediction
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Machine Learning Reading Group @ CUED
Unpacking UK’s New AI Action Plan: Ambition versus Reality
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Machine Learning Reading Group @ CUED
An Introduction to Algorithmic Differentiation
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
An Introduction to Algorithmic Differentiation
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Machine Learning Reading Group @ CUED
Foundation Models in Robotics
Machine Learning Reading Group @ CUED
Learning curve prediction for AutoML
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Learning curve prediction for AutoML
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Machine Learning Reading Group @ CUED
An Elementary Introduction to Sequential Monte Carlo Samplers
Machine Learning Reading Group @ CUED
Scalable Sampling Using Annealed Algorithms
Machine Learning Reading Group @ CUED
Are we making progress in unlearning?
Machine Learning Reading Group @ CUED
Natural Experiments in NLP and Where to Find Them
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Machine Learning Reading Group @ CUED
Linear Attention for Efficient Transformers
Machine Learning Reading Group @ CUED
Task Alignment
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Machine Learning @ CUED
Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis
Machine Learning @ CUED
Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis
Machine Learning @ CUED
Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis
Machine Learning Reading Group @ CUED
A Light Introduction to Topological Data Analysis
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Machine Learning Reading Group @ CUED
Mean Field Theory of NNs
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Machine Learning Reading Group @ CUED
Discussing the Stanford AI Report
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Machine Learning Reading Group @ CUED
Discussing the Stanford AI Report
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Machine Learning Reading Group @ CUED
AI Control
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Machine Learning Reading Group @ CUED
Neural likelihood-free inference
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Machine Learning Reading Group @ CUED
An Introduction to the Conjugate Gradient Method
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Machine Learning Reading Group @ CUED
An Introduction to Transformer Neural Processes
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Machine Learning @ CUED
Consistent Validation for Predictive Methods in Spatial Settings
Machine Learning Reading Group @ CUED
Learning Symmetries in Neural Networks
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Machine Learning Reading Group @ CUED
GenCast: Diffusion-based ensemble forecasting for medium-range weather (or: How to ruin a numerical weather forecaster’s Christmas)
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Machine Learning Reading Group @ CUED
Bayesian coresets
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Machine Learning Reading Group @ CUED
Flow matching, stochastic interpolants and everything in between
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Machine Learning @ CUED
Modern Bayesian Experimental Design
Machine Learning Reading Group @ CUED
Challenges of Regulating Increasingly Complicated Human-AI Collaborative Systems
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Machine Learning Reading Group @ CUED
A Poisson Process Model for Monte Carlo
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Learning Directed Acyclic Graphs (DAGs) With Continuous Optimization
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Deep Learning for Medium-Range Global Weather Prediction
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Machine Learning Reading Group @ CUED
Learning linear models in-context with transformers
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Machine Learning Reading Group @ CUED
Quasi-Monte Carlo: structure in the randomness for better sampling
Machine Learning @ CUED
Visit and talk by Jay McClelland: "Some thoughts on the differences between human and machine intelligence"
Machine Learning @ CUED
Compositional mathematics and automatic gradient descent
Machine Learning Reading Group @ CUED
Deciphering Batch Effects in Single-cell Transcriptomics with Concept Bottlenecks
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Schrödinger bridges, diffusion and SDEs
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
The LLM Tidal Wave
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
The LLM Tidal Wave
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Game theory, distributional reinforcement learning, control and verification
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
On choosing the mass matrix for Hamiltonian Monte Carlo
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Reward Modelling
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning @ CUED
Convergence bounds for the Random Walk Metropolis algorithm - Perspectives from Isoperimetry
Machine Learning Reading Group @ CUED
Navigating the Future: Upcoming EU AI Regulation and its Potential Impact on the Field
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Learning-based multiscale modeling: computing, data science, and uncertainty quantification
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Neural Tangent Kernel
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning Reading Group @ CUED
No-regret Dynamics for Multi-agent Learning
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Machine Learning Reading Group @ CUED
Scalable Approaches to Self-Supervised Learning using Spectral Analysis
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning Reading Group @ CUED
Physics-informed machine learning
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Machine Learning Reading Group @ CUED
Causal Machine Learning
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
User Manipulation in Recommender Systems
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Machine Learning Reading Group @ CUED
Random Features for Kernel Approximation
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning Reading Group @ CUED
An Overview of Differential Privacy, Membership Inference Attacks, and Federated Learning
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning Reading Group @ CUED
Bayesian Neural Networks
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning Reading Group @ CUED
Offline Reinforcement Learning
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning Reading Group @ CUED
{PF}^2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization Under Unknown Constraints
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning @ CUED
Calls to F the Algorithm: Lessons from the 2020 Exam Debacle
Machine Learning Reading Group @ CUED
Scalable simulation and inference in non-Gaussian stochastic PDEs
Machine Learning @ CUED
Scalable simulation and inference in non-Gaussian stochastic PDEs
Machine Learning @ CUED
Measuring Alignment Between Perceptual Systems: An Analysis Through The Lens of Shared Invariances
Machine Learning Reading Group @ CUED
Information Geometry — Natural Gradient Descent
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning @ CUED
Gaussian processes, spectral analysis kernels and optimal transport
Machine Learning @ CUED
Deep Reinforcement Learning for Multi-Agent Interaction
Machine Learning Reading Group @ CUED
Predicting generalization of ML models.
⚠️ Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Machine Learning @ CUED
Presenting Hawk-Eye’s Skeletrack: Our machine learning approach to building a real time skeletal tracking system for sports, and how we're using it to shape the future of fan engagement
Machine Learning Reading Group @ CUED
A tutorial on diffusion models
Machine Learning Reading Group @ CUED
Benchmarking and evaluation in contemporary machine learning
Machine Learning Reading Group @ CUED
Advanced artificial agents intervene in the provision of reward
Machine Learning Reading Group @ CUED
Theory and Practice of Infinitely Wide Neural Networks - Guest Talk
Machine Learning Reading Group @ CUED
The unreasonable effectiveness of mathematics in large scale deep learning
Machine Learning Reading Group @ CUED
The role of meta-learning for few-shot classification
Machine Learning @ CUED
Noise-Aware Differentially Private Synthetic Data
Machine Learning Reading Group @ CUED
Benefits and Shortcomings of Assistance
Machine Learning @ CUED
Estimating RSV seasonality from pandemic disruptions: a modelling study
Machine Learning Reading Group @ CUED
Discussion: Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Machine Learning Reading Group @ CUED
Discussion: Learning PAC-Bayes Priors for Probabilistic Neural Networks
Machine Learning Reading Group @ CUED
Rethinking evaluation for machine learning models
Machine Learning Reading Group @ CUED
Discussion: Bayesian Model Selection, the Marginal Likelihood, and Generalization
Machine Learning @ CUED
Beyond Conformal Prediction: Distribution-Free Uncertainty Quantification for Complex Machine Learning Tasks
Machine Learning Reading Group @ CUED
How close are these distributions? A brief introduction to statistical distances and divergences.
Machine Learning Reading Group @ CUED
Bests of ICLR
Machine Learning @ CUED
Graph neural network approach for decentralized multi-robot coordination
Machine Learning Reading Group @ CUED
Machine Learning with Quantum Computers
Machine Learning Reading Group @ CUED
Autoregressive Diffusion Models
Machine Learning Reading Group @ CUED
An Overview of AI Alignment
Machine Learning Reading Group @ CUED
The Bayesian Learning Rule for Adaptive AI
Machine Learning Reading Group @ CUED
Introduction to differential privacy
Machine Learning Reading Group @ CUED
Retrieval Augmented NLP
Machine Learning Reading Group @ CUED
Circuits and Interpretability
Machine Learning Reading Group @ CUED
Optimal Transport Metrics
Machine Learning Reading Group @ CUED
Learned Compression
Machine Learning Reading Group @ CUED
Diffusion and Score-based Generative Models
Machine Learning Reading Group @ CUED
AI Safety
Machine Learning Reading Group @ CUED
[Cancelled] Best of Neurips
Machine Learning Reading Group @ CUED
Energy-Based Models
Machine Learning Reading Group @ CUED
Deep Kernels
Machine Learning Reading Group @ CUED
Mean Field Theory of NN (postponed to March 2022)
Machine Learning Reading Group @ CUED
Representation Learning: A Causal Perspective
Machine Learning Reading Group @ CUED
Ethics, Integrity and Good Practice in ML
Machine Learning Reading Group @ CUED
Implicit Regularization in Deep Learning
Machine Learning Reading Group @ CUED
Planning Meeting + PI Presentations
Machine Learning Reading Group @ CUED
Towards Neuro-Causality: Relating Graph Neural Networks to Structural Causal Models
Machine Learning Reading Group @ CUED
CBL Alumni Talk - Task-specific routing of information in neural circuits via structured noise by Cristina Savin
Machine Learning Reading Group @ CUED
CBL Alumni Talk: Nonlinear filtering as a unifying principle in neuroscience by Jean-Pascal Pfister
Machine Learning Reading Group @ CUED
CBL Alumni Talk: Finale Doshi-Velez
Machine Learning Reading Group @ CUED
Pseudo-Points and State-Space Gaussian Processes
Machine Learning Reading Group @ CUED
CBL Alumni Talk: Latent Stochastic Differential Equations: An Unexplored Model Class.
Machine Learning Reading Group @ CUED
The Statistical Finite Element Method
Machine Learning Reading Group @ CUED
CBL Alumni Talk - Our Power as Technical Designers
Machine Learning Reading Group @ CUED
Best of ICLR
Machine Learning Reading Group @ CUED
CBL Alumni Talk: Accurate Gaussian Processes and how they can help Deep Learning
Machine Learning Reading Group @ CUED
An Introduction to PAC-Bayes
Machine Learning Reading Group @ CUED
Automated Augmented Conjugate Inference for Gaussian Processes
Machine Learning Reading Group @ CUED
CBL Alumni Talk: Examining Critiques in Bayesian Deep Learning
Machine Learning Reading Group @ CUED
Online Learning and Online Convex Optimisation
Machine Learning Reading Group @ CUED
Monte Carlo Gradient Estimation in Machine Learning
Machine Learning Reading Group @ CUED
Secondmind's research activities to make Gaussian Processes industry proof
Machine Learning Reading Group @ CUED
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Machine Learning Reading Group @ CUED
Gaussian Processes I have Known
Machine Learning Reading Group @ CUED
Causal Representation Learning
Machine Learning Reading Group @ CUED
Long-Range Transformers
Machine Learning Reading Group @ CUED
Inference in Stochastic Processes
Machine Learning Reading Group @ CUED
Fairness in AI
Machine Learning Reading Group @ CUED
Variational Bayes as Surrogate Regression
Machine Learning Reading Group @ CUED
Bayesian optimization / Gaussian Process Bandits
Machine Learning Reading Group @ CUED
Meta-reinforcement learning
Machine Learning Reading Group @ CUED
Best of NeurIPS
Machine Learning Reading Group @ CUED
Neural Processes
Machine Learning Reading Group @ CUED
Large-scale sequential experimentation
Machine Learning Reading Group @ CUED
Symmetries in Reinforcement Learning
Machine Learning Reading Group @ CUED
Gradient-based Hyperparameter Optimisation
Machine Learning Reading Group @ CUED
Best Papers from ICML 2020
Machine Learning @ CUED
Efficient and Structured Uncertainty: Challenges and Opportunities
Machine Learning Reading Group @ CUED
Active Learning
Machine Learning Reading Group @ CUED
Recent advances in the theory and applications of VAEs
Machine Learning Reading Group @ CUED
Best of ICLR 2020
Machine Learning Reading Group @ CUED
Differentiable Planning
Machine Learning Reading Group @ CUED
Recent Developments in Bayesian Deep Learning
Machine Learning Reading Group @ CUED
Strategic Classification
Machine Learning Reading Group @ CUED
Machine Learning on Sets
Machine Learning Reading Group @ CUED
Boltzmann Generators and Stochastic Normalizing Flows
Machine Learning Reading Group @ CUED
World Models
Machine Learning Reading Group @ CUED
Federated Learning
Machine Learning @ CUED
Game Playing Meets Game Theory: Strategic Learning from Simulated Play
Machine Learning Reading Group @ CUED
Self-Supervised Representation Learning
Machine Learning Reading Group @ CUED
Kernel Mean Embeddings
Machine Learning Reading Group @ CUED
Neural Tangent Kernel
Machine Learning @ CUED
Robust Deep Learning Under Distribution Shift
Machine Learning Reading Group @ CUED
Scalable Gaussian Processes
Machine Learning Reading Group @ CUED
Probabilistic Programming
Machine Learning Reading Group @ CUED
Neural Attention
Machine Learning Reading Group @ CUED
Equivariance and Symmetries in CNNs
Machine Learning @ CUED
Models and inference for temporal Gaussian processes
Machine Learning Reading Group @ CUED
Hyperparameter Optimisation
Machine Learning Reading Group @ CUED
Meta-Learning or "Learning To Learn"
Machine Learning @ CUED
Reinforcement Learning at Huawei: Robustness, Safety, and Efficiency
Machine Learning @ CUED
Constructing temporal latent spaces: Representation learning for clustering and imputation on time series
Machine Learning @ CUED
Explaining Neural Networks: Post-hoc and Natural Language Explanations
Machine Learning Reading Group @ CUED
Hamiltonian Monte Carlo for Hierarchical Models
Machine Learning @ CUED
Learning-Algorithms from Bayesian Principles
Machine Learning @ CUED
Rotation Invariant Householder Parameterization for Bayesian PCA
Machine Learning @ CUED
Two Approximate Sampling Methods for Bayesian Deep Learning
Machine Learning @ CUED
Semi-Unsupervised Learning with Deep Generative Models / Disentangling Improves VAEs' Robustness to Adversarial Attacks
Machine Learning @ CUED
Efficiency and Transferability of Neural Networks
Machine Learning @ CUED
Learning via Data Compression: Bayesian Coresets and Sparse Variational Inference
Machine Learning @ CUED
Deep Neural Networks: A Nonparametric Bayesian Approach with Local Competition
Machine Learning @ CUED
Information Consumption on Social Media: Efficiency, Trust, and Divisiveness
Machine Learning @ CUED
Why Do We Need New Hardware & Software for Machine Intelligence?
Machine Learning Reading Group @ CUED
Particle MCMC
Machine Learning Reading Group @ CUED
Stochastic Differential Equations
Machine Learning @ CUED
Nonparametric Generative Modeling via Optimal Transport and Diffusions with Provable Guarantees
Machine Learning Reading Group @ CUED
Generalisation in neural networks
Machine Learning Reading Group @ CUED
An Overview of Normalizing Flows
Machine Learning @ CUED
NeVAE: A Deep Generative Model for Molecular Graphs
Machine Learning @ CUED
Global model explainability via aggregation
Machine Learning Reading Group @ CUED
Sampling as Optimization
Machine Learning @ CUED
Robust machine learning for causal inference in health care
Computational Neuroscience
The K-FAC method for neural network optimization
Machine Learning Reading Group @ CUED
Implicit Variational Inference
Machine Learning Reading Group @ CUED
Continual Learning: Definitions, Benchmarks, and Approaches
Machine Learning Reading Group @ CUED
Causal Inference and Causal Reinforcement Learning
Machine Learning Reading Group @ CUED
Generative models for few-shot prediction tasks
Machine Learning @ CUED
Online Meta-Learning
Machine Learning Reading Group @ CUED
Logical Uncertainty
Computational Neuroscience
High-dimensional dynamics of generalization error in neural networks: implications for experience replay
Machine Learning @ CUED
Gauge Equivariant Convolutional Networks on Manifolds
Machine Learning Reading Group @ CUED
Stein Discrepancy
Machine Learning @ CUED
Observation and Intervention Incentives in Causal Influence Diagrams: Towards an Understanding of Powerful Machine Learning Systems
Machine Learning @ CUED
Fairness for Sequential Decision Making Algorithms
Machine Learning @ CUED
Gaussian processes for inferring latent functions in complex data models
Machine Learning Reading Group @ CUED
Neural Ordinary Differential Equations
Machine Learning Reading Group @ CUED
Natural gradient in deep neural networks
Machine Learning Reading Group @ CUED
Graph Neural Networks
Machine Learning Reading Group @ CUED
Defending Against Adversarial Attacks
Machine Learning Reading Group @ CUED
Reinforcement Learning and Control as Probabilistic Inference
Machine Learning @ CUED
Causal Inference for Treatment Effects: A Theory and Associated Learning Algorithms
Machine Learning @ CUED
Interpretability - the myth, questions, and some answers
Machine Learning @ CUED
Constraint-based causal Discovery from NOnstationary/heterogeneous Data (CD-NOD)
Machine Learning @ CUED
Fast yet Simple Natural-Gradient Variational Inference in Complex Models
Machine Learning @ CUED
Variance in Policy Gradient methods and Learning Sequential Latent Variable Models
Machine Learning @ CUED
Structured deep models: Deep learning on graphs and beyond
Machine Learning @ CUED
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Machine Learning @ CUED
Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features
Machine Learning @ CUED
Algorithmic Glass Ceiling in Social Networks
Machine Learning @ CUED
A Bayesian Perspective on Generalization and SGD
Machine Learning @ CUED
Algorithmic Glass Ceiling in Social Networks
Machine Learning Reading Group @ CUED
Neural Networks and Natural Language Processing
Machine Learning @ CUED
Structure in tensor-variate data: a trivial byproduct of simpler phenomena?
Machine Learning Reading Group @ CUED
Information Theory, Codes, and Compression
Machine Learning @ CUED
Lipschitz Global Optimization
Machine Learning Reading Group @ CUED
Statistical Learning Theory
Machine Learning Reading Group @ CUED
Deep Generative Models
Machine Learning Reading Group @ CUED
Minimum Description Length
Machine Learning Reading Group @ CUED
Machine Learning for Sounds
Machine Learning Reading Group @ CUED
NIPS 2017 Highlights
Machine Learning @ CUED
Variational inference for some models with Polya-Gamma latent variables and Gaussian process priors
Machine Learning @ CUED
Bayesian Generative Adversarial Networks
Machine Learning @ CUED
AI for Inclusive Finance
Machine Learning Reading Group @ CUED
Hardware Efficient Machine Learning
Machine Learning @ CUED
Backprop through the Void: Optimizing Control Variates for Black-Box Gradient Estimation.
Machine Learning @ CUED
Learning to Learn without Gradient Descent by Gradient Descent
Machine Learning Reading Group @ CUED
Learning to Learn
Machine Learning @ CUED
Towards true end-to-end learning & optimization
Machine Learning Reading Group @ CUED
WGAN and Optimal Transport
Machine Learning Reading Group @ CUED
Interpretability in Machine Learning
Machine Learning Reading Group @ CUED
Deep Structured Prediction for Handwriting Recognition
Machine Learning @ CUED
Targeted Disclosure to Support Auditing and Accountability for Automated Decision-making
Machine Learning Reading Group @ CUED
Cooperative Inverse RL
Machine Learning Reading Group @ CUED
Infer.NET
Machine Learning @ CUED
Nonlinear ICA using temporal structure: a principled framework for unsupervised deep learning
Machine Learning @ CUED
Transfer Learning for NLP
Machine Learning @ CUED
A Bayesian Treatment for Uncertainty -- and its application in health care
Machine Learning @ CUED
Unbiased Estimation of the Eigenvalues of Large Implicit Matrices
Machine Learning @ CUED
The Grammar Variational Autoencoder & Counterfactual Fairness
Machine Learning @ CUED
Accelerating computation of SVM and DNN by binary approximation
Machine Learning @ CUED
Towards User-Friendly Image Inpainting: Learning-to-Rank based Image Quality Assessment for Image Inpainting
Machine Learning @ CUED
Deep learning for autonomous driving
Machine Learning @ CUED
Understanding Black-box Predictions via Influence Functions
Machine Learning @ CUED
A simple neural network module for relational reasoning
Machine Learning @ CUED
Future technology: machine learning using memristors networks
Machine Learning @ CUED
Future technology: machine learning using memristors networks
Machine Learning @ CUED
On Different Distances Between Distributions and Generative Adversarial Networks
Machine Learning Study Group
Deep Learning Book - Meeting 9 - Representation Learning
Machine Learning Study Group
Deep Learning Book - Meeting 8 - Autoencoders
Machine Learning Study Group
Deep Learning Book - Meeting 7 - Neural Turing Machines & Conditional Random Fields as RNNs
Machine Learning Reading Group @ CUED
Kernel Mean Embeddings
Information Engineering Division seminar list
Bayesian Optimization for Probabilistic Programs
Machine Learning Study Group
Deep Learning Book - Meeting 6 - Recurrent Neural Networks
Machine Learning @ CUED
Differentially Private Bayesian Learning
Computational and Biological Learning Seminar Series
Control, inference and learning
Machine Learning Reading Group @ CUED
Differential Privacy
Machine Learning @ CUED
Differential Privacy Tutorial
Machine Learning Study Group
Deep Learning Book - Meeting 5 - ResNets and DenseNets
Machine Learning @ CUED
Cooperative Inverse Reinforcement Learning
Machine Learning Study Group
Deep Learning Book - Meeting 4 - Convolutional Networks
Machine Learning Reading Group @ CUED
Probabilistic Numerics
Machine Learning Study Group
Deep Learning Book - Meeting 3 - Optimization
Machine Learning @ CUED
Bayesian optimisation in many dimensions with bespoke probabilistic programs
Machine Learning Study Group
Deep Learning Book - Meeting 2 - Regularization
Machine Learning Study Group
Deep Learning Book - Meeting 1 - Deep Feedforward NNs
Machine Learning @ CUED
Variational Bayes In Private Settings
Machine Learning @ CUED
Julia: Introduction and new developments
Machine Learning Reading Group @ CUED
NIPS 2016 papers in 5mins
Machine Learning @ CUED
Bayesian optimality and frequentist extended admissibility are equivalent in saturated models
Machine Learning @ CUED
Hierarchical Multi-Agent Reinforcement Learning through Communicative Actions for Human-Robot Collaboration
Machine Learning @ CUED
Variational autoencoders with latent graphical models
Machine Learning Reading Group @ CUED
Symmetry in Statistical Models
Machine Learning @ CUED
Learning with limited supervision
Machine Learning Reading Group @ CUED
Bayesian Optimization
Machine Learning @ CUED
A tale of P-matrices and TripleSpinners - the unreasonable effectiveness of structured models in nonlinear embeddings
Machine Learning @ CUED
Rejection Sampling Variational Inference
Machine Learning Reading Group @ CUED
deep generative models
Machine Learning @ CUED
Probabilistic Numerical Computation: A New Concept?
Machine Learning Reading Group @ CUED
Sketching methods
Machine Learning @ CUED
A talk in two parts: (1) AI Neuroscience: How much do deep neural networks understand about the images they classify? (2) Robots that can adapt like animals.
⚠️ Note venue: James Dyson Building
Machine Learning Reading Group @ CUED
A crash-course on Bayesian Reinforcement Learning
Machine Learning @ CUED
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
Machine Learning @ CUED
Robots learning on the move: deep learning from lots of demonstration
Machine Learning @ CUED
Probabilistic modeling for position and orientation estimation using inertial sensors
Machine Learning @ CUED
Moment matching for latent variable models: from ICA to LDA and CCA
Machine Learning @ CUED
Multiresolution Matrix Factorization
Machine Learning @ CUED
Dynamic Models for Health Data
Machine Learning @ CUED
Learning with Memory Embeddings
Machine Learning @ CUED
Data Driven Discrete Time Modeling of Continuous Time Nonlinear Systems: Problems, Challenges, Success Stories
Machine Learning @ CUED
Inference as Learning
Machine Learning @ CUED
Interpretable and interactive machine learning
Machine Learning @ CUED
Discriminative Embeddings of Latent Variable Models for Structured Data
Machine Learning @ CUED
Structured Dynamic Graphical Models & Scaling Multivariate Time Series Methodology
Machine Learning @ CUED
Scaling and Generalizing Approximate Bayesian Inference
arg58's list
A modular architecture for Unicode text compression
Machine Learning @ CUED
Modeling the Dynamics of Online Learning Activity
Machine Learning @ CUED
Developments in Exact Inference in Graphical Models
Machine Learning @ CUED
Approximation strategies for structure learning in Bayesian networks
⚠️ Note: via Skype!
Machine Learning @ CUED
Automated Reasoning and AI for Large Formal Mathematics
Machine Learning Reading Group @ CUED
Partially Observable Markov Decision Processes (POMDPs)
Machine Learning @ CUED
Variational inference for scalable Gaussian process approximations
Machine Learning @ CUED
Novel MCMC and SMC schemes for Poisson-Kingman Bayesian Nonparametric mixture models
Machine Learning @ CUED
Turing: Rejuvenating Probabilistic Programming in Julia
Machine Learning @ CUED
Towards Weaker Supervision and Simpler Pipelines in Speech Recognition
Machine Learning Reading Group @ CUED
Learning polynomials with Neural Networks
Machine Learning @ CUED
Sum-Product Networks for Probabilistic Modeling
Machine Learning @ CUED
MLE-Struct: Bethe Learning of Graphical Models
Machine Learning Reading Group @ CUED
An introduction to Bayesian nonparametrics: some inference schemes for infinite mixture models
Machine Learning Reading Group @ CUED
Topics in Expectation Propagation
Machine Learning Reading Group @ CUED
Deep Q-Learning and AlphaGo
Machine Learning Reading Group @ CUED
inference failures in probabilistic programming
Machine Learning Reading Group @ CUED
Variational Methods and Compressed Sensing
Machine Learning @ CUED
Inference and Learning in the Anglican Probabilistic Programming System
Machine Learning Reading Group @ CUED
Fast Fusion of Multi-band Images: A Powerful Tool for Super-resolution
Machine Learning @ CUED
Unsupervised Risk Estimation with only Structural Assumptions
Computational Neuroscience
It's the Network Dummy: Exhuming the reticular theory while shoveling a little dirt on the neuron doctrine
Machine Learning @ CUED
The geometry of uncertainty
Machine Learning Reading Group @ CUED
Probabilistic programming
Machine Learning Reading Group @ CUED
Learning by learning rich generative models
Machine Learning @ CUED
A more Automated Statistician
Machine Learning Reading Group @ CUED
Natural gradient descent and variational inference
Machine Learning Reading Group @ CUED
Differentiable Data Structures and (if we have time) POMDPs
⚠️ NOTE Topic Change
Machine Learning Reading Group @ CUED
Random projection ensemble classification
Machine Learning Reading Group @ CUED
Deep learning for time series
Machine Learning Reading Group @ CUED
Stochastic discrete integration
Machine Learning @ CUED
General Reinforcement Learning
Machine Learning Reading Group @ CUED
Stochastic optimization and adaptive learning rates
Machine Learning Reading Group @ CUED
Logic, Theorem Proving, and Probabilistic Programming
Machine Learning Reading Group @ CUED
Connections between kernels, GPs, and NNs
Machine Learning @ CUED
Inference of a partially observed kinetic Ising model
Machine Learning Reading Group @ CUED
Detecting paraphrases using recursive autoencoders
Machine Learning Reading Group @ CUED
Bayesian optimization and its applications
Machine Learning Reading Group @ CUED
LP relaxations for MAP inference
Machine Learning @ CUED
Meta-Bayesian Analysis
Machine Learning @ CUED
Efficient Inference and Learning with Intractable Posteriors? Yes, Please.
Machine Learning @ CUED
Efficient multi-task Gaussian process models for genome-wide association studies
Machine Learning @ CUED
Harmonic Exponential Families and Group-Equivariant Convolution Networks
Machine Learning @ CUED
Higher Order Fused Regularization for Supervised Learning with Grouped Parameters
Machine Learning @ CUED
A-Star Sampling Review
Machine Learning @ CUED
Convex Factorization Machines
Machine Learning @ CUED
Belief and Truth in Hypothesised Behaviours
Machine Learning @ CUED
Training and Understanding Deep Neural Networks for Robotics, Design, and Perception
Machine Learning @ CUED
Information-Theoretic Bounded Rationality
Machine Learning @ CUED
The limits of MAP inference by MWSS on perfect graphs
Machine Learning @ CUED
Scalable Gaussian Processes for Scientific Discovery
Machine Learning @ CUED
MCMC for non-linear state space models using ensembles of latent sequences
⚠️ (via Skype!)
Machine Learning @ CUED
Gradient-based hyperparameter optimization through reversible learning
Machine Learning @ CUED
Rich Component Analysis
Machine Learning @ CUED
A new MCMC hybrid scheme for Poisson-Kingman Bayesian Nonparametric mixture models
Machine Learning @ CUED
Extreme Classification: A New Paradigm for Ranking & Recommendation
Machine Learning @ CUED
Convex and non-convex worlds in machine learning
Machine Learning @ CUED
Random Function Classes for Machine Learning
Machine Learning @ CUED
Explaining Non-Linear Classifier Decisions with application to Deep Learning
Machine Learning @ CUED
Deep Gaussian processes and variational propagation of uncertainty
Machine Learning @ CUED
Deep Learning
Machine Learning @ CUED
Random Function Classes for Machine Learning
Machine Learning @ CUED
Unsupervised Learning with Latent Variable Models
Machine Learning @ CUED
Unsupervised Learning with Latent Variable Models
Machine Learning @ CUED
Structural Markov laws / Geometry and HMC
Machine Learning @ CUED
Mean Field Approaches to Two Sequential Decision Making Problems.
⚠️ This talk will be given via Skype
Machine Learning @ CUED
Do Deep Nets Really Need to be Deep?
Machine Learning Reading Group @ CUED
Convolutional Neural Networks
Machine Learning Reading Group @ CUED
Hessian-free Optimisation for Neural Networks
Machine Learning @ CUED
Probabilistic numerics: treating numerical computation as learning, or; it's Bayes all the way down
Machine Learning Reading Group @ CUED
Scalable MCMC
Machine Learning @ CUED
Machine Learning for Quantitative Finance: A collaboration between the Cambridge Machine Learning Group and Cambridge Capital Management
Machine Learning Reading Group @ CUED
Causal Inference
Machine Learning @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Gaussian Process Models for Time Series
Machine Learning @ CUED
Inference for infinite mixture models and Gaussian Process mixtures of experts using simple approximate MAP Inference
Machine Learning Reading Group @ CUED
Informational Geometry
Machine Learning Reading Group @ CUED
Deep Gaussian Processes
Machine Learning Reading Group @ CUED
Combinatorial Stochastic Processes in Bayesian Nonparametrics
Machine Learning @ CUED
Evolutionary dynamics in a continuous public goods game
Machine Learning @ CUED
An introduction to the Mondrian Process
Machine Learning @ CUED
Population Inference for Functional Brain Connectivity
Microsoft Research Machine Learning and Perception Seminars
Workshop: Machine Learning in Cambridge 2015
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Probabilistic approaches to understanding bird conversations
Machine Learning @ CUED
A* Sampling
Machine Learning Reading Group @ CUED
Variance Reduction Techniques and Stochastic Optimisation for Monte Carlo
Machine Learning @ CUED
Orthologous networks in biological systems
Machine Learning Reading Group @ CUED
Transfer Learning
Machine Learning @ CUED
Latent Branching Trees
Machine Learning @ CUED
Experiments with Non-parametric Topic Models
Machine Learning Reading Group @ CUED
Advanced HMC
Machine Learning Reading Group @ CUED
Active Learning
Machine Learning @ CUED
The Blended Paradigm: A Bayesian Approach to Handling Outliers and Misspecified Models
Machine Learning Reading Group @ CUED
Deep Probabilistic Models (Wake/Sleep)
Machine Learning @ CUED
Machine Translation with LSTMs
⚠️ Room changed to LR3B in Inglis building and time changed to 10:30
Machine Learning Reading Group @ CUED
PAC Bayes
Machine Learning @ CUED
Oracle Variational Inference
Machine Learning Reading Group @ CUED
LSTM and Recurrent Neural Networks
Machine Learning @ CUED
Bayesian modeling for high-level real nursing activity recognition using accelerometers
⚠️ short talk
Machine Learning Reading Group @ CUED
Automatic Differentiation with Theano
Machine Learning Reading Group @ CUED
State Space Abstraction for Reinforcement Learning
Machine Learning Reading Group @ CUED
Kernel Embedding for Distributions
Machine Learning @ CUED
A Tutorial on Probabilistic Programming
CUED Control Group Seminars
Optimal control and optimal sampling: A statistical physics perspective.
Machine Learning @ CUED
New Methods in Bayesian Optimization for Machine Learning
Machine Learning @ CUED
A marginal sampler for σ-Stable Poisson-Kingman mixture models
Machine Learning @ CUED
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
Microsoft Research Machine Learning and Perception Seminars
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Unsupervised Many-to-many Object Matching
Machine Learning @ CUED
Probabilistic Numerics - a snapshot of an emerging community
Machine Learning @ CUED
On the Bethe approximation
Machine Learning @ CUED
Gaussian process regression on graphs
Machine Learning @ CUED
Implicit Representation Networks
Machine Learning @ CUED
Scalable Deep Gaussian Processes
Machine Learning Reading Group @ CUED
Unsupervised Representation Learning
Machine Learning @ CUED
Active Learning of Linear Embeddings for Gaussian Processes
Machine Learning @ CUED
Bayesian monitoring for the Comprehensive Nuclear-Test-Ban Treaty
Machine Learning @ CUED
Unifying logic and probability: A "New Dawn" for Artificial Intelligence?
⚠️ NOTE THE SPECIAL VENUE (Microsoft Research)
Machine Learning Reading Group @ CUED
Time-varying dynamic Bayesian network reconstruction with information sharing
Machine Learning Reading Group @ CUED
Advanced Gaussian Process approximation methods
Machine Learning @ CUED
Practical Machine Learning at Facebook. Examples and Lessons Learnt.
⚠️ Room changed to LR4
Machine Learning @ CUED
Bayesian inference for integer-valued Lévy processes with Non-Gaussian Ornstein-Uhlenbeck volatility modelling
⚠️ short talk (30 mins) - NOTE TIME CHANGE
Machine Learning Reading Group @ CUED
Statistical model criticism
Machine Learning @ CUED
Density Estimation in Infinite Dimensional Exponential Families
Machine Learning @ CUED
Stable Poisson-Kingman species sampling priors generated by general ordered size biased generalized gamma mixing distributions
Machine Learning Reading Group @ CUED
Functional Programming
Machine Learning Reading Group @ CUED
The Bernoulli Factory problem and some connections with Computable Analysis
Machine Learning Reading Group @ CUED
Neural Network Language Modelling
Machine Learning @ CUED
Probabilistic computing applications: BayesDB and stochastic digital circuits
Machine Learning Reading Group @ CUED
Loopy belief propagation
Machine Learning @ CUED
Probabilistic computing for Bayesian inference
Machine Learning @ CUED
Machine Learning and Order Book Dynamics
Machine Learning @ CUED
Policy Evaluation with Temporal Differences
Machine Learning @ CUED
Matrix Means, Distances, Kernels, and Geometric Optimization
Machine Learning @ CUED
Learning to Learn for Structured Sparsity
Machine Learning @ CUED
Bayesian nonparametric dynamic-clustering and genetic imputation
Machine Learning @ CUED
Parameter estimation in deep learning architectures: Two new insights.
⚠️ Note: time is 12 noon (*not* 11am)
Machine Learning @ CUED
Bayesian canonical correlation analysis
Machine Learning @ CUED
Cambridge - Tuebingen PhD Applicant Talks (via Skype)
⚠️ Interview talks via Skype
Machine Learning @ CUED
Generative probabilistic programming: applications and new ideas
Machine Learning @ CUED
Bayesian nonparametrics: Dependency and Constraint Modeling
⚠️ Note: TALK via Skype!
Machine Learning @ CUED
Bayesian Nonparametric Model for Power Disaggregation
Machine Learning @ CUED
Probabilistic machine learning for knowledge extraction from videos and text
⚠️ Note venue: LR6
Machine Learning Reading Group @ CUED
An introduction to Sequential Monte Carlo
Machine Learning @ CUED
Frequentist coverage of adaptive nonparametric Bayesian credible sets
Machine Learning Reading Group @ CUED
Probabilistic Data Structures and Algorithms
Machine Learning Reading Group @ CUED
Information Theory and Method of Types: Channels, Quantizers, and Divergences
Machine Learning @ CUED
Anglican; Particle MCMC inference for Probabilistic Programs
Machine Learning @ CUED
Sparse discriminative latent characteristics for predicting cancer drug sensitivity
Microsoft Research Machine Learning and Perception Seminars
Bayesian Machine Learning for Controlling Autonomous Systems
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Integrated Nested Laplace Approximation (INLA)
Machine Learning Reading Group @ CUED
Spectral Learning
Machine Learning Reading Group @ CUED
Random Projections
Machine Learning @ CUED
Contrastive Learning Using Spectral Methods
Machine Learning Reading Group @ CUED
Herding: Driving Deterministic Dynamics To Learn And Sample Probabilistic Models
Machine Learning Reading Group @ CUED
Bayesian and Bandit Optimization
Machine Learning @ CUED
Nonparametric Bayesian statistics with exchangeable random structures
Machine Learning @ CUED
Dissecting genotype to phenotype relationships
Microsoft Research Machine Learning and Perception Seminars
Compositionality in Vision and Language
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
CANCELLED: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
⚠️ CANCELLED
Machine Learning @ CUED
Clustering Based on Predictive Variances in Gaussian Process Regression Models
Microsoft Research Machine Learning and Perception Seminars
Foundations of Neuromechanical Systems Biology: Combining engineering, biology, and mathematics to understand how we move
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Strategies for General Recognition
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
The cost of principles
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Non-Parametric Conditional Random Fields in Computer Vision and Image Processing
Machine Learning @ CUED
Higher Order Learning for Classification in Emergency Situations
Microsoft Research Machine Learning and Perception Seminars
Lifts of Convex Sets and Cone Factorizations
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Annealing Between Distributions by Averaging Moments
Microsoft Research Machine Learning and Perception Seminars
Efficient Multi-dimensional Parametric Mincuts for Constrained MAP Inference
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
NLIP Seminar Series
Recursive Deep Learning for Modeling Semantic Compositionality
Microsoft Research Machine Learning and Perception Seminars
Noise estimation by PDE-constrained optimisation
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Random Forests: One tool for all your problems.
Machine Learning @ CUED
Applied statistical genetics and next-generation association studies
Machine Learning @ CUED
Applied statistical genetics and next-generation association studies
Machine Learning Reading Group @ CUED
Modelling Nonlinear Dynamical Systems
Machine Learning @ CUED
The combinatorial structure of conditionally i.i.d. negative binomial processes directed by a beta process
Machine Learning Reading Group @ CUED
Adaptive Hamiltonian-based MCMC samplers
jg691's list
Google's Approach to Building Relationships with Universities: Presentation and Talk by Dr David J Harper
Machine Learning Reading Group @ CUED
Bayesian Nonparametrics in Real-World Applications: Statistical Machine Translation and Language Modelling on Big Datasets
Machine Learning @ CUED
Matrix Concentration Inequalities via the Method of Exchangeable Pairs
Machine Learning @ CUED
Deep Gaussian Processes
Microsoft Research Machine Learning and Perception Seminars
Building Better Questionnaires with Probabilistic Modelling
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Approaches to statistical modeling of network data
Machine Learning Reading Group @ CUED
Sparsity: Beyond L1
Machine Learning @ CUED
Bayesian nonparametric methods for non-exchangeable data
Machine Learning Reading Group @ CUED
Reproducing Kernel Hilbert Spaces in Non-parametric Statistics
Machine Learning @ CUED
Non-parametric Bayesian Chromatin State Segmentation
Machine Learning Reading Group @ CUED
An Introduction to Sum Product Networks
Microsoft Research Machine Learning and Perception Seminars
Austerity in MCM - Land : Cutting the computational Budget
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Fast Variational Inference in the Conjugate Exponential Family
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Fast Variational Inference in the Conjugate Exponential Family
Machine Learning Reading Group @ CUED
Bayesian Reinforcement Learning
Microsoft Research Machine Learning and Perception Seminars
Semantics\sqcap Scalability\models\bot? Are Semantics and Scalability Incompatible?
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Probabilistic Programming
Microsoft Research Machine Learning and Perception Seminars
Warped Mixture Models for Meaningful Clustering and Bayesian Manifold Learning
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
An application of HDP And IBP for stream-based action recognition and high dimensional data
Machine Learning @ CUED
Using Context and Insight for the Analysis of LittleData?
Machine Learning Reading Group @ CUED
Machine Learning in Speech Recognition
Machine Learning Reading Group @ CUED
Fragmentation Coagulation
Machine Learning @ CUED
Feature allocations, probability functions, and paintboxes
Machine Learning @ CUED
Modelling Reciprocating Relationships with Hawkes Processes
Machine Learning Reading Group @ CUED
Conditional Density Estimation
Microsoft Research Machine Learning and Perception Seminars
New probabilistic methods for inference of natural selection on regulatory sequences in the human genome
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Daniel’s Adventures in Computer Vision Land
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
RCC Planning
Machine Learning Reading Group @ CUED
Discrete Optimization
Machine Learning @ CUED
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables
Machine Learning Reading Group @ CUED
Advanced Sampling
Machine Learning Reading Group @ CUED
NIPS Recap
Machine Learning Reading Group @ CUED
NIPS Recap
Microsoft Research Machine Learning and Perception Seminars
From motion capture of interacting hands to video based rendering.
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Bayesian and Gradient Reinforcement Learning
Microsoft Research Machine Learning and Perception Seminars
Spectral Edge: Making the Invisible Visible
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Spectral Clustering
Machine Learning @ CUED
The combinatorial structure underlying a beta processes is that of a continuum of Blackwell-MacQueen urn schemes
Machine Learning Reading Group @ CUED
Modern Neural Networks: the Hinton Camp
Machine Learning @ CUED
Learning of Milky Way Model Parameters Using Matrix-variate Data in a New Gaussian Process-based Method
Machine Learning Reading Group @ CUED
Distributional compositional models of semantics
Machine Learning @ CUED
Identification of causal effects
Microsoft Research Machine Learning and Perception Seminars
Towards ad hoc interactions with robots
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Probabilistic methods for biomolecular structure simulations
Machine Learning Reading Group @ CUED
Completely Random Measures in Bayesian Nonparametrics
Computer Laboratory Systems Research Group Seminar
Distributed, Real-Time Bayesian Learning in Online Services
Microsoft Research Machine Learning and Perception Seminars
From the Information Extraction Pipeline to Global Models, and Back
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Model selection in a large compositional space
⚠️ note: the first 45 mins will be planning and the talk will start at around 3:15pm
Machine Learning @ CUED
Compressed Sensing Applications in Functional Magnetic Resonance Imaging
Microsoft Research Cambridge, public talks
Convergent and Scalable Algorithms for Expectation Propagation Approximate Bayesian Inference
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Multi-Label Learning with Millions of Categories
Machine Learning @ CUED
Efficient Sampling with Kernel Herding
Microsoft Research Machine Learning and Perception Seminars
Capture of dynamic scene using multiple cameras provides rich spatial-temporal information that can be used for solving challenging computer vision problems.
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Tell Me Where You’ve Lived and What You Want -- I’ll Tell You What You Like and Need
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Latent Hough Transform for Object Detection
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Frank-Wolfe optimization insights in machine learning
Microsoft Research Machine Learning and Perception Seminars
Context sensitive information: Which bits matter in data?
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Deep learning for vision: a case study for visual textures, and some thoughts on a general framework
Machine Learning @ CUED
Thermodynamics as a Theory of Decision-Making with Information Processing Costs
Microsoft Research Machine Learning and Perception Seminars
SmartDesign: Living with Geometric Complexity
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Human Behavior Classification with Infinite Hidden Conditional Random Fields
Microsoft Research Machine Learning and Perception Seminars
Part A: Non-parametric image optimization & Part B: Crowdsourcing gaze data collection
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Multi-Label Learning with Millions of Categories & Generalized Multiple Kernel Learning with a Million Kernels
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Inference Group
Data Modelling With Neural Networks (II): Content-Addressable Memories And State-Of-The-Art Error-Correcting Codes
⚠️ Last Lecture!
Microsoft Research Machine Learning and Perception Seminars
Prediction Strategies without Loss
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Fast Gaussian process learning for regression, semi-supervised classification, and multiway analysis
Machine Learning @ CUED
Non-parametric Bayesian Learning of User Preferences: Elicitation, Sparsification and Beyond
Machine Learning @ CUED
Structured Prediction using Linear Programming Relaxations
Inference Group
Advanced Monte Carlo Methods and Variational Inference
Machine Learning Reading Group @ CUED
Dependent normalized random measures
Machine Learning Reading Group @ CUED
GP-BUCB for Spinal Cord Injury Therapy: Batch Active Learning with Applications
Machine Learning @ CUED
Discovery of Complex Behaviors through Contact-Invariant Optimization
Inference Group
Approximating Probability Distributions (I)-(II): Monte Carlo Methods and Variational Inference
⚠️ Two lectures!
Microsoft Research Machine Learning and Perception Seminars
The Inverted Multi-Index
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
On Data (In-)Dependent Hashing
Inference Group
An Introduction to Statistical Inference, Data Modelling & Pattern Recogntion
⚠️ Two Lectures!
Machine Learning Reading Group @ CUED
Poisson Processes: Applications in Machine Learning
Machine Learning Reading Group @ CUED
A rough guide to the Aldous-Hoover representation theorem for exchangeable arrays
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Dirichlet Process Mixture Models and Bayesian Nonparametric Density Estimation
Machine Learning @ CUED
Learning with nonparametric dependence and divergence estimation
Inference Group
Communication over noisy channels (II)
Machine Learning @ CUED
Past work and future interests: respectively, the scattering of Anyons and Monte Carlo methods
Machine Learning Reading Group @ CUED
"Structured sparsity and convex optimization"
Machine Learning @ CUED
Non-parametric Bayesian Method and Maximum-A-Posteriori Inference in Statistical Machine Translation
Microsoft Research Machine Learning and Perception Seminars
Topic Models for Human Activity Understanding
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Inference Group
Communication over noisy channels (I)
Machine Learning Reading Group @ CUED
Financial Problems tractable to Machine Learning Methods
Machine Learning Reading Group @ CUED
A Predictive Study of Bayesian Nonparametric Regression Models
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Weighted Finite-state Automata
Machine Learning Reading Group @ CUED
Active Learning
Machine Learning Reading Group @ CUED
Topic Modelling
Machine Learning Reading Group @ CUED
Structural Learning of Dynamic Bayesian Networks
Microsoft Research Machine Learning and Perception Seminars
ORCHID: Human-agent collectives for disaster response and the smart grid
⚠️ o This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attendin
Machine Learning Reading Group @ CUED
Extended ensemble Monte Carlo
Machine Learning @ CUED
Infinite Structured Explicit Duration Hidden Markov Models
Machine Learning Reading Group @ CUED
"Symmetry and sufficiency"
Inference Group
Entropy and data compression (II)
⚠️ PLEASE NOTE: There will be no lectures during the Easter break (12 March - 9 April). Lectures will resume on 16 April.
Microsoft Research Machine Learning and Perception Seminars
Sequential Decision Making in Experimental Design and Sustainability via Adaptive Submodularity
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Inference Group
Entropy and data compression (I)
Machine Learning @ CUED
Probabilistic computing: computation as universal stochastic inference, not deterministic calculation
Inference Group
Introduction to information theory
Microsoft Research Machine Learning and Perception Seminars
Exploiting Variable Impedance for Robotics: Mimic or Optimize?
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Information bottleneck
⚠️ Please note the change in time. This RCC will take place at 15:00-16.30pm.
Machine Learning Reading Group @ CUED
Ensemble Methods in Machine Learning
Machine Learning @ CUED
Scaling Machine Learning for the Internet
Microsoft Research Machine Learning and Perception Seminars
Closing the gap between weakly and fully supervised methods
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Bayesian Quadrature for Prediction and Optimisation
Machine Learning @ CUED
Efficient MCMC for Continuous Time Discrete State Systems
Microsoft Research Machine Learning and Perception Seminars
Beyond the Piece of Cardboard: Learning to Adjust Photographs
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
A Maximum Entropy Perspective on Spectral Dimensionality Reduction
Machine Learning Reading Group @ CUED
Approximate Inference in Gaussian Process Models
Microsoft Research Machine Learning and Perception Seminars
PARIS: Probabilistic Alignment of Relations, Instances, and Schema
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Probabilistic programs and the computability and complexity of Bayesian reasoning
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning Reading Group @ CUED
Proper local scoring rules
Machine Learning @ CUED
Variational Inference for Non-Conjugate Models
Machine Learning Reading Group @ CUED
Hyper and structural Markov laws for graphical models
Machine Learning @ CUED
Not so naive Bayesian classification
Microsoft Research Machine Learning and Perception Seminars
Exclusive Pólya Urns and their applications
⚠️ o This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attendin
Machine Learning @ CUED
Bayesian Nonparametrics: Latent Feature and Prediction Models, and Efficient Inference
Machine Learning @ CUED
Machine Learning Markets
Machine Learning @ CUED
Factored Shapes and Appearances for Parts-based Object Understanding AND Transformation Equivariant Boltzmann Machines
Machine Learning @ CUED
Optimal Reinforcement Learning for Gaussian Systems
Machine Learning @ CUED
Exclusive Pólya Urns and their applications
Microsoft Research Machine Learning and Perception Seminars
Information-Greedy Global Optimisation
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
Mechanism Design without Money via Stable Matching
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Microsoft Research Machine Learning and Perception Seminars
SocialFusion: Fusing Mobile, Sensor, and Social Computing in the Cloud To Enable Next-Generation Context-Aware Applications
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Approximate Bayesian Inference for Large Scale Inverse Problems: A Computational Viewpoint
Machine Learning @ CUED
Some Practical Reflections on Graphical Models
Microsoft Research Machine Learning and Perception Seminars
Principles of Humanoid Locomotion Control
⚠️ This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
Machine Learning @ CUED
Beyond Keyword Search: Discovering Relevant Scientific Literature
Machine Learning @ CUED
Graphical Models for Bandit Problems
Machine Learning @ CUED
Bayesian regression and classification with multivariate sparsifying priors
Machine Learning @ CUED
An FX trading system using adaptive reinforcement learning
⚠️ (rescheduled from May 24th)
Machine Learning @ CUED
Censored Exploration in Dark Pools
Machine Learning Reading Group @ CUED
Connections between Gaussian Process Regression, Kalman filtering and RTS Smoothing
Machine Learning @ CUED
An FX trading system using adaptive reinforcement learning
⚠️ RESCHEDULED FOR JUNE 7th
Machine Learning Reading Group @ CUED
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
Inference Group
Hierarchical hidden Markov models revisited
Machine Learning Reading Group @ CUED
Expectation Propagation for POMDP Spoken Dialogue Models
Microsoft Research Machine Learning and Perception Seminars
Formal Methods in Synthetic Biology
Inference Group
Mandarin Dasher
Machine Learning @ CUED
Characterization of the Ewens-Pitman family of random partitions by a deletion property and a de Finetti-type theorem for exchangeable hierarchies
Microsoft Research Machine Learning and Perception Seminars
Enforcing topological constraints in energy-based image segmentation
Microsoft Research Machine Learning and Perception Seminars
On a first-order primal-dual algorithm with applications to convex problems in computer vision
Microsoft Research Cambridge, public talks
Machine Learning for WLAN Positioning
Inference Group
Rigid body motion in conformal geometric algebra
Machine Learning @ CUED
Nonlinear Dynamics of Learning
Machine Learning Reading Group @ CUED
Exchangeability
Machine Learning @ CUED
Exponential Conditional Volatility Models
Inference Group
CRH: a Central Reference Handling system
Machine Learning Reading Group @ CUED
Deep Belief Networks for Phone Recongition
Machine Learning @ CUED
Challenges in implementing the Bayesian paradigm
Machine Learning @ CUED
Expectation Propagation in Sparse Linear Models with Spike and Slab Priors
Machine Learning @ CUED
Probabilistic matrix factorization for reconstruction of missing data
Machine Learning Reading Group @ CUED
The effect of normalization -- a case study in speech synthesis
Machine Learning @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Post-NIPS Highlight session
Machine Learning Reading Group @ CUED
Optimal weighted nearest neighbour classifiers
Machine Learning Reading Group @ CUED
Kernel Methods
Inference Group
A computer-vision based facial gesture switch
Machine Learning Reading Group @ CUED
Machine Learning techniques in computer vision applications
Machine Learning @ CUED
Differential Geometric MCMC Methods
Machine Learning Reading Group @ CUED
Infinite multiple relational models for complex networks
Machine Learning @ CUED
Mining viral datasets
Machine Learning Reading Group @ CUED
Completely Random Measures
Machine Learning @ CUED
Universal Bayesian Agents: Theory and Applications
Machine Learning @ CUED
Machine learning in cancer research (a.k.a CRI meets CUED)
⚠️ (note new date)
Machine Learning @ CUED
Novi Quadrianto
Inference Group
Introduction to Geometric Algebra II
Machine Learning Reading Group @ CUED
Submodularity for Machine Learning
Microsoft Research Machine Learning and Perception Seminars
Handling temporal variation of unknown characteristics in streaming data analysis.
Machine Learning @ CUED
Learning item trees for collaborative filtering with implicit feedback
Inference Group
Title to be confirmed
Machine Learning @ CUED
Non-Smooth-Norm Image Reconstruction from Noisy Data
Machine Learning Reading Group @ CUED
NIPS Highlight Session
Machine Learning Journal Club
Machine Leaning Journal Club
Machine Learning Reading Group @ CUED
Poisson Processes
Machine Learning @ CUED
Crowdsourcing data modelling
Inference Group
Out-of-band Language Modelling
Machine Learning Reading Group @ CUED
Convex Optimisation
Microsoft Research Machine Learning and Perception Seminars
Bayesian Inference with Kernels
Inference Group
South Africa's Involvement With Square Kilometre Array Bid: Infrastructure and Electromagnetic Quietness
Machine Learning @ CUED
Short talks: Mixed Cumulative Distribution Networks; Nonparametric Bayesian community discovery in social networks; Expectation Propagation for Dirichlet Process Mixture Models
Machine Learning Reading Group @ CUED
An Introduction to Bayesian Statistics
Microsoft Research Machine Learning and Perception Seminars
Affective Technology
Inference Group
The Multiscale Composite Dirichlet Process and its applications
Physics of Medicine (PoM) Seminar Series
STORM below the localisation limit
Inference Group
Putting Some Language Back Into Language Modelling
Machine Learning Reading Group @ CUED
Herding or a '3rd way to learn'
Machine Learning Reading Group @ CUED
Advanced Scientific Programming in Python
Inference Group
Bayesian Psychometry
Machine Learning Reading Group @ CUED
Unsupervised Grammar Induction
Inference Group
Continuous Gesture Recognition and Visualization
Machine Learning @ CUED
Searching for Knowledge Instead of Web Pages
Machine Learning @ CUED
Bayesian Inference with Kernels
Inference Group
Title to be confirmed
Inference Group
Ticker: Case Study
Machine Learning @ CUED
On the Equivalence of Graph Cuts and Max-product Belief Propagation
Inference Group
Statistical Language Modelling
Inference Group
Title to be confirmed
Inference Group
Topic Models -- and how to break them
Microsoft Research Cambridge, public talks
Information Asymmetries in Pay-Per-Bid Auctions: How Swoopo Makes Bank
Wednesday Seminars - Department of Computer Science and Technology
Some Uses of Hashing in Networking Problems
Machine Learning @ CUED
Culture wars, voting and polarization: divisions and unities in modern American politics
⚠️ general audience talk (non-technical) -- NOTE change of room to LR4!
Machine Learning @ CUED
Creating structured and flexible models: some open problems
Machine Learning @ CUED
Efficient Bayesian analysis of multiple changepoint models
Inference Group
Approximate Inference for the Loss-Calibrated Bayesian
Machine Learning @ CUED
Learning Common Grammar from Multilingual Corpus / Online Multiscale Dynamic Topic Models
Machine Learning @ CUED
Scalable Parallel Computing with CUDA
Inference Group
Introduction to Geometric Algebra
Inference Group
Dasher on a Space-filling curve
Machine Learning @ CUED
Using transformed domains to sparsify Gaussian Processes
Machine Learning @ CUED
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Inference Group
Bayesian head pose estimation
Inference Group
Differential Privacy and Probabilistic Inference
Machine Learning @ CUED
Natural Conjugate Gradient Learning for Fixed-Form Variational Bayes
Machine Learning @ CUED
Sparse Factor Analysis Applied to Three Biological Problems
Machine Learning @ CUED
Using topic models to help cure cancer
Machine Learning Reading Group @ CUED
Structured Learning and Structural SVMs
Machine Learning @ CUED
Structured Prediction Cascades
Machine Learning @ CUED
Parametric Bandits, Query Learning, and the Haystack Dimension
Machine Learning Reading Group @ CUED
Conditional Random Fields : Theory and Application
Inference Group
Robust Associative memory
Inference Group
Conditional Counting: Approximate Multinomial Probit Regression
Machine Learning Reading Group @ CUED
Topics in Statistical Machine Translation
Machine Learning Reading Group @ CUED
Title to be confirmed
Inference Group
Coherent Inference on Optimal Play in Games
Inference Group
Report from CHI 2010
Machine Learning Reading Group @ CUED
Bundle methods and its application in machine learning
Inference Group
Speech Dasher: Fast Writing using Speech and Gaze
Inference Group
Language Modelling with PPM
Machine Learning Reading Group @ CUED
Sparsification for Gaussian Processes for Regression
Machine Learning @ CUED
Slice sampling with latent Gaussian models
Inference Group
Bayesian feature tracking and head pose estimation
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning @ CUED
Dynamic Network Tomography: Model, Algorithm, Theory, and Application
Machine Learning @ CUED
Making Sense of Data - A Research Agenda
Machine Learning @ CUED
Subspace Codes for Adversarial Error-Correction in Network Coding
⚠️ short talk
Machine Learning @ CUED
Bayesian Inference in Networks of Queues
Machine Learning @ CUED
Stochastic Outlier Selection
Inference Group
Neurally-inspired Computing
Inference Group
Latent Force Models with Gaussian Processes
Inference Group
Dasher
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Semi supervised learning
Machine Learning Reading Group @ CUED
Variational inference in graphical models: The view from the marginal polytope
Inference Group
NIPS 2009 highlights
Machine Learning Reading Group @ CUED
The Fractional Belief Propagation menace
HEP phenomenology joint Cavendish-DAMTP seminar
Accelerated Bayesian inference in cosmology
Machine Learning Reading Group @ CUED
(Canceled) A Causal Calculus for Statistical Research
⚠️ Canceled
Machine Learning @ CUED
Machine Learning Course (4F13)
⚠️ 4F13 course - repeats Wed, Thurs at 10am during Lent
Machine Learning Reading Group @ CUED
RCC Planning Meeting
Machine Learning @ CUED
Machine Learning Course (4F13)
⚠️ 4F13 course - repeats Wed, Thurs at 10am during Lent
Inference Group
Title to be confirmed
Machine Learning Reading Group @ CUED
Unbounded-depth hierarchical Pitman-Yor processes
Inference Group
NIPS poster
Inference Group
Face Recognition: Who's That Blonde?
Machine Learning Journal Club
Journal Club: Visualizing Data using t-SNE
Machine Learning Reading Group @ CUED
Learning rates in Bayesian nonparametrics
Inference Group
Probabilistic feature tracking with non-probabilistic techniques
Machine Learning @ CUED
Indian Buffet Processes with Power-law Behaviour
Machine Learning Journal Club
The Infinite Gaussian Mixture Model
Machine Learning @ CUED
Gaussian Processes for Active Data Selection, Faults, Changepoints and Sensor Selection
Inference Group
研究の紹介
Machine Learning Reading Group @ CUED
Bayesian Agglomerative Clustering with Coalescents
Machine Learning @ CUED
KL control theory and decision making under uncertainty
Machine Learning Journal Club
Oscillations, Phase-of-firing Coding and STDP: An Efficient Learning Scheme (Journal club)
Machine Learning Reading Group @ CUED
Machine Learning RCC - Bayesian Agglomerative Clustering with Coalescents, Teh, Daumé and Roy, NIPS 2007
Machine Learning Reading Group @ CUED
Filtering of Noisy Time-Series Data
Inference Group
Optimizing Dasher for the iPhone
Machine Learning Journal Club
Bayesian integration in sensorimotor learning
Machine Learning Reading Group @ CUED
Speed Reviewing
Machine Learning @ CUED
Information theoretic model selection in clustering
Inference Group
A facial gesture switch using hidden Markov models
Machine Learning Journal Club
Distilling Free-Form Natural Laws from Experimental Data
Machine Learning Reading Group @ CUED
High-dimensional variable selection via sure independence screening
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Inclusive user modelling
Machine Learning Journal Club
Journal club: Measurement integration under inconsistency for robust tracking
Machine Learning Reading Group @ CUED
Ensemble Methods in Machine Learning
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Learning Logical Relations
Machine Learning @ CUED
Motor Skills Learning for Robotics
Machine Learning Reading Group @ CUED
Structured prediction using energy-based models
Inference Group
Recognition and Correction of Voice Web Search Queries
Machine Learning Journal Club
The Mondrian Process
Machine Learning Reading Group @ CUED
Technical Writing II
Machine Learning @ CUED
Coconut: Optimizing computations for machine learning
Inference Group
Of Uncertain Value -- Bayesian Quadratic Reinforcement Learning
Machine Learning Journal Club
COGAIN'09 proceedings
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Capacity of Spiking Neural Networks
Machine Learning Journal Club
Speed dependent automatic zooming for browsing large documents
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Thermal expansion of beta-eucryptite
Machine Learning Journal Club
Single Image Haze Removal using Dark Channel Prior
Machine Learning Journal Club
Bursty and Hierarchical Structure in Streams.
Machine Learning @ CUED
Convex Variational Bayesian Inference for Large Scale Generalized Linear Models
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Text entry performance of state of the art unconstrained handwriting recognition: a longitudinal user study
Machine Learning Journal Club
Correlation, Causation, Model Comparison and Interventions
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Combining Collaborative Filtering with Meta Data for Scalable Recommendations
⚠️ Note unusual time
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning @ CUED
Computable Probability Theory
Inference Group
Physics Dimensions in Sage
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Dashing this way and that
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
Mapping facial gestures to control cursors and switches
Machine Learning Reading Group @ CUED
Hessian-based Markov-Chain Monte Carlo Algorithms
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Inference Group
A quick way to learn a mixture of exponentially many linear models
⚠️ Note unusual time
Inference Group
Short ICML Practice Talk
Machine Learning Reading Group @ CUED
Slice Sampling
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning @ CUED
Using gradient descent for optimization and learning
Machine Learning Reading Group @ CUED
Technical Writing
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Optimal Bayesian Reinforcement Learning on Trees
Machine Learning Reading Group @ CUED
Divergence measures and message passing
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Reading Group @ CUED
The IBM approach to speech separation
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Reading Group @ CUED
Parsing Images the UCLA Way
Inference Group
Useful Computation with Coincidence Detection
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Reading Group @ CUED
Hilbert space embedding of probability distributions
Inference Group
Bayesian periodicity detection
Inference Group
Deducing Principles in Natural Perception
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning @ CUED
Learning from Measurements in Exponential Families
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Reading Group @ CUED
Bregman Divergences and Machine Learning
Inference Group
Geothermal power in Australia
Inference Group
Cloud Ships for global warming reduction
Inference Group
Painless Parse Errors
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning @ CUED
Shrinkage regression for multivariate inference with missing data, with an application to portfolio balancing
Machine Learning @ CUED
Quasi-linear Sensor Management
Machine Learning Reading Group @ CUED
The Convex Concave Procedure
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning Reading Group @ CUED
Hybrids of Generative and Discriminative Models (Cont)
Machine Learning Journal Club
Machine Learning Book Reading Club
Optimization and Incentives Seminar
A Survey of Results for Deletion Channels and Related Synchronization Channels
⚠️ Joint With Probability Seminar Series.
Kuwait Foundation Lectures
Bloom Filters, Related Data Structures, and their Applications
Machine Learning @ CUED
Generalization in Learning
Machine Learning Reading Group @ CUED
Large Margin Training of Hidden Markov Models
Machine Learning Journal Club
Machine Learning Book Club
Machine Learning @ CUED
Stochastic control as an inference problem
Machine Learning Reading Group @ CUED
Hybrid of Generative and Discriminative Models
Machine Learning Reading Group @ CUED
Divergence measures and message passing - CANCELLED
⚠️ Canceled
Inference Group
More NIPS highlights
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Predicting and understanding the stability of G-Quadruplexes
Machine Learning @ CUED
Extending the Affinity Propagation Model
Machine Learning Reading Group @ CUED
Distributed Computing for Machine Learning
Inference Group
NIPS Highlights
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Reading Group @ CUED
A Hierarchical Bayesian Language Model based on Pitman-Yor Processes
Machine Learning @ CUED
The Block Diagonal Infinite Hidden Markov Model
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning @ CUED
Probabilistic Graph Models for Debugging Software
Machine Learning Reading Group @ CUED
Probabalistic Inference for solving (PO)MDPs
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning @ CUED
HMMs for Protein Sequencing from Mass Spectrometry Data
Machine Learning Reading Group @ CUED
NIPS Offline Conference
Inference Group
Optimizing Similarity Functions in Various Pattern Recognition Problems
⚠️ Note unusual time
Inference Group
Visual Search: Applications and Computational Modelling
Inference Group
Eye-Tracking in Virtual Reality
Machine Learning @ CUED
Mondrian Processes
Inference Group
Estimating the Pen Trajectories of Handwritten Static Scripts using Hidden Markov Models
Machine Learning Reading Group @ CUED
An Introduction to Relational Learning
Cavendish Physical Society
Power to Choose: How the energy choices people make will change their lives.
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Journal Club: Mixed Membership Stochastic Blockmodels
Machine Learning Reading Group @ CUED
Bayesian Reinforcement Learning in Continuous POMDPs
Inference Group
Bayesian Inductive Programming
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning @ CUED
Hardness Ratios
Machine Learning Reading Group @ CUED
Cluster Analysis of Heterogeneous Rank Data
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Value Propagation: A Graphical Model for Bayesian Reinforcement Learning
Machine Learning Reading Group @ CUED
Gaussian Process Latent Variable Models
Inference Group
Thermochemistry and Nuclear power - Clean responsive electricity
Machine Learning @ CUED
Consensus finding, exponential models and infinite rankings
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Spiking Boltzmann Machines
Machine Learning Reading Group @ CUED
Point Process Intensity Estimation with GP's
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
A Precision-Click Method for Multiple-Choice Problems
Machine Learning Reading Group @ CUED
Knows What It Knows: A Framework For Self-Aware Learning
Inference Group
Low-cost large-scale renewable energy
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part III)
Machine Learning Journal Club
The discovery of structural form
Machine Learning Reading Group @ CUED
Approximating the Kullback-Leibler Divergence Between GMMs
Wednesday Seminars - Department of Computer Science and Technology
Forensic Genomics:Kin Privacy, Driftnets and Other Open Questions
Machine Learning @ CUED
Efficient Sequential Monte Carlo Inference for Kingman's Coalescent
⚠️ Note unusual time
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning @ CUED
Context in human robot interaction
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part II)
Machine Learning Reading Group @ CUED
To Naive Bayes or to Logistically Regress: That is the Question
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part I)
Machine Learning @ CUED
Shared Segmentation of Natural Scenes using Dependent Pitman-Yor Processes
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Reading Group @ CUED
Condition Monitoring
Machine Learning @ CUED
Spoken Dialogue Management
Machine Learning @ CUED
A Bayesian approach to language learning
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning @ CUED
Learning Bigrams from Unigrams
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Reading Group @ CUED
Levy Processes
Machine Learning @ CUED
Nonparametric Bayesian Natural Language Model Domain Adaptation: A Hierarchical, Hierarchical Pitman-Yor Process Language Model
Inference Group
Clustering by linear programming, convex optimization and belief propagation
⚠️ Note unusual time
Machine Learning @ CUED
Bayesian approaches to autonomous Bayesian real-time learning
Machine Learning Reading Group @ CUED
The War on Loops
Machine Learning @ CUED
Non-negative matrix factorization with Gaussian process priors
Machine Learning @ CUED
Matrix Factorization and Relational Learning
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Machine Learning Book Reading Club
Inference Group
Artificial intelligence in combinatorial game design
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Machine Learning Book Reading Club
Inference Group
From Automated Currency Validation to Protein Fold Recognition: Probabilistic Multi-class Multi-kernel Learning
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Journal Club
Machine Learning Book Reading Club
Inference Group
Modeling with Bounded Partition Functions
Machine Learning @ CUED
Double Feature: Optimal Precoding for MIMO and Divergence Estimation for Continuous Distributions
Machine Learning @ CUED
Nonparametric Bayesian Learning of Switching Dynamical Systems
Machine Learning @ CUED
Message-passing inference on graphical models
Machine Learning Journal Club
Machine Learning book reading club
Microsoft Research Cambridge, general interest public talks
The Geometry of the Space of Shapes
⚠️ Hosted by Prof. Andrew Blake (Microsoft Research)
Machine Learning Journal Club
Machine Learning Book Reading Club
Machine Learning Reading Group @ CUED
Variational Bayesian Mixtures of Gaussians
Machine Learning @ CUED
Bayesian analysis of complex biological systems
Inference Group
Bayesian Models for Dependency Parsing Using Pitman-Yor Priors
Machine Learning @ CUED
Variational inference for partially observed diffusion processes
Machine Learning @ CUED
Assessing high-dimensional latent variable models
Machine Learning Journal Club
Cutting Cheese
Machine Learning Journal Club
Uniform sampling from various sets
Machine Learning @ CUED
Identification of dynamical subpopulations, optimal experiment design and more
Inference Group
Gaussian Process Density Sampler
Machine Learning Reading Group @ CUED
Stochastic integration and Ito's lemma
Machine Learning @ CUED
H-Infinity Clustering
Machine Learning @ CUED
Sustainable Energy - without the hot air
Machine Learning Journal Club
Journal Club: The Dynamic Hierarchical Dirichlet Process
Machine Learning Reading Group @ CUED
Continuous Time Bayesian Networks
Machine Learning Journal Club
Object-Attribute Models
Machine Learning @ CUED
Modeling Behaviour in Economic Games using Game-Theoretic POMDPs
Machine Learning @ CUED
Talking with Robots: A Case Study in Architectures for Cognitive Robotics
Machine Learning Journal Club
Measuring Game Temperature With UCT-Monte Carlo
Machine Learning @ CUED
An Introduction to Statistical Learning Theory
⚠️ Machine Learning Tutorial Lecture
Inference Group
Noise in the central nervous system
Machine Learning Journal Club
Tutorial on Monte Carlo methods
Inference Group
On Sparsity and Overcompleteness in Image Models
Inference Group
Treed Gaussian Processes for Regression and Classification
⚠️ Note new date and time.
Machine Learning Journal Club
Expectation Propagation Demonstration
Machine Learning @ CUED
Beam Sampling for Infinite Hidden Markov Models
Machine Learning @ CUED
Learning quantum physics
Machine Learning Journal Club
Cheap and cheerful neural inference
Machine Learning Journal Club
On the Benefits of Confidence Visualization in Speech Recognition
Inference Group
(clashing event: Sustainable Energy and Arithmetic)
⚠️ Computer Lab seminar clashes with normal group meeting
Machine Learning @ CUED
Convergence analysis of the EM algorithm and joint minimization of free energy
Machine Learning @ CUED
Discriminative Methods with Structure
Machine Learning Journal Club
Expectation Propagation on the TrueSkill model
Machine Learning Reading Group @ CUED
'All of Nonparametric Statistics'
Machine Learning @ CUED
A Bayesian approach to network modularity: inferring the structure and scale of modular networks
Machine Learning Journal Club
Hierarchical Clustering
Machine Learning Reading Group @ CUED
The Information Bottleneck / Dynamic Kernals for speaker verification
Inference Group
How nervous systems are designed to be energy efficient
Machine Learning Journal Club
Image superresolution
Machine Learning @ CUED
Model selection and model order adaptation for clustering
Machine Learning @ CUED
Expectation Propagation, Experimental Design for the Sparse Linear Model
Machine Learning @ CUED
Biomedical Image Search
Machine Learning Journal Club
Infera - a Bayesian team-ranking program from 1996
Machine Learning Reading Group @ CUED
Probabilistic Matrix Factorization/Deep Belief Nets
Inference Group
Bayesian learning of visual chunks by human observers
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning @ CUED
Stable distribution and data sketching
Machine Learning Journal Club
Principled Hybrids of Generative and Discriminative Models
Machine Learning Reading Group @ CUED
Spectral methods
Inference Group
Non-Rigid Photometric Stereo with Colored Lights
Machine Learning @ CUED
Reinforcement Learning
⚠️ Machine Learning Tutorial Lecture (Note unusual day)
Machine Learning Journal Club
Sparse Feature Learning for Deep Belief Networks
Machine Learning Reading Group @ CUED
Active Learning
Machine Learning @ CUED
Modeling Science: Topic models of Scientific Journals and Other Large Document Collections
Machine Learning @ CUED
Sparse Gaussian Process in Disease Mapping
Machine Learning Journal Club
Semantic Hashing, Deep Autoencoders and Universal Approximators
Machine Learning Reading Group @ CUED
Bayesian Adaptive Inference and Adaptive Training
Inference Group
NIPS presentations
Inference Group
The development of a motor network in Drosophila, the role of precocious neural activity in the emergence of coordination
Machine Learning @ CUED
Gene Regulatory Network Inference: A Kernel-Based Learning Approach
Machine Learning Journal Club
Reinforcement Learning in continuous state-spaces
Machine Learning Reading Group @ CUED
Hierarchical Dirichlet Processes
Machine Learning @ CUED
Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
Inference Group
Life beyond the Physics department
Machine Learning @ CUED
Machine Learning Applications / Challenges in Natural Language Parsing
⚠️ Machine Learning Tutorial Lecture
Machine Learning Reading Group @ CUED
Bayesian Reinforcement Learning
Machine Learning Journal Club
Variational methods
Machine Learning Reading Group @ CUED
Transductive and Semi-Supervised Learning
Machine Learning @ CUED
An overview of covariance operators in Hilbert space, and their applications
Machine Learning @ CUED
Optimal Control and Reinforcement Learning with Gaussian Process Models
Machine Learning Journal Club
Loopy Belief Propagation
Machine Learning Reading Group @ CUED
Variational inference and exponential families
Inference Group
The last thing that we should talk about
Machine Learning Journal Club
Basics of Belief Propagation
Machine Learning Reading Group @ CUED
Sparse Bayesian Linear Models
Inference Group
Bliss-Dasher -- Efficient semantic writing with any muscle
Machine Learning @ CUED
Hidden Common Cause Relations in Relational Learning
Machine Learning Journal Club
Fast Visual Tracking By Temporal Consensus
Machine Learning Reading Group @ CUED
Autonomous Agents under Operational Closure
Machine Learning Journal Club
Sensible Priors for Sparse Bayesian Learning
Machine Learning @ CUED
Optimal Spreading Sequences for Chaos-Based Communication Systems; Using CSK as a Case Study
Machine Learning Journal Club
Hierarchical Bayesian inference in networks of spiking neurons
Machine Learning @ CUED
Graph Kernels for Data Mining
Machine Learning Journal Club
Predictive Interaction using the Delphian Desktop
Machine Learning @ CUED
Geometric Algorithms for Linear Independent Component Analysis
Machine Learning @ CUED
Covariate Shift Adaptation: Supervised Learning When Training and Test Inputs Have Different Distributions
Inference Group
Five energy plans for Britain
Microsoft Research Machine Learning and Perception Seminars
Physics-Based Human Motion Models for Animation and Tracking
Machine Learning Journal Club
Combining Causal and Similarity-based reasoning
Machine Learning Journal Club
Efficient Bayesian Task-Level Transfer Learning
Inference Group
What is a Fab Lab?
Machine Learning Journal Club
Kingman's coalescent, non-parametric Bayesian agglomerative clustering
Machine Learning Journal Club
Contextual dependencies in unsupervised word segmentation
Microsoft Research Machine Learning and Perception Seminars
Bayesian network structure learning from uncertain interventions
Machine Learning Journal Club
Pre-Gatsby Workshop
Machine Learning Journal Club
Knowledge Compilation
Machine Learning Journal Club
Clustering by Passing Messages Between Data Points
Machine Learning Journal Club
Title Language Model
Machine Learning Journal Club
Bring your Papers!
Machine Learning Reading Group @ CUED
Kingman's coalescent, non-parametric Bayesian agglomerative clustering, and ICML 2007
Machine Learning Reading Group @ CUED
Sensible priors from finite linear models
⚠️ Note unusual time
Microsoft Research Machine Learning and Perception Seminars
Generative models for audio and music processing
Machine Learning Reading Group @ CUED
Statistical Models for Partial Membership
Machine Learning Reading Group @ CUED
Infinite ICA and Information Retrieval
Inference Group
Language Modelling Discussion
Inference Group
Open Vocabulary Confusion Networks for Speech Recognition
Microsoft Research Machine Learning and Perception Seminars
identity variables for face recognition: from distance based methods to probabilistic inference
Machine Learning Reading Group @ CUED
Overlapping Clusters and 4th-year Projects
Inference Group
An Error-Correcting Keyboard
Inference Group
Probabilities, Complexities and Game Semantics
Machine Learning Reading Group @ CUED
What can Gaussian Processes do for Reinforcement Learning?
Machine Learning Reading Group @ CUED
System Conditioning vs Explicit Bayes Inference, and Collaborative LDA
Inference Group
Learning and Stability in Random Sigma-Pi Networks
Machine Learning Reading Group @ CUED
Affinity Propagation and Hierarchical Beta Processes
⚠️ Room changed, Note unusual time
Inference Group
Shape writing: fluid text entry on mobile devices
Machine Learning Reading Group @ CUED
Optimal Learning
Inference Group
Bayesian nonparametric latent feature models
Machine Learning Reading Group @ CUED
Gaussian Approximations for Binary Gaussian Process Classification, and Hidden Topic Markov Models
Inference Group
Context Tree Weighting
Machine Learning Reading Group @ CUED
Function Approximation in MDPs
Machine Learning Reading Group @ CUED
Change Point Problems in Linear Dynamical Systems
⚠️ Postponed (originally April 5)
Microsoft Research Machine Learning and Perception Seminars
A Bayesian model that links microarray mRNA measurements to mass spectrometry protein measurements
Probabilistic Systems, Information, and Inference Group Seminars
Analysis of Channel Coding for Redundant Sources
Machine Learning Reading Group @ CUED
GP-LVMs
Inference Group
Inductive Logic Programming and Kernel methods
Inference Group
Logistic regression with a Laplacian prior on the singular values: convex duality and application to EEG classification.
⚠️ Note unusual time and location
Machine Learning Reading Group @ CUED
Topics in Convex Optimisation
Inference Group
Monte Carlo Approaches to Game Playing
Microsoft Research Machine Learning and Perception Seminars
Motion Tracking
Machine Learning @ CUED
Logistic Regression with a Laplacian prior on the Eigenvalues: Convex duality and application to EEG classification
⚠️ Note different room and time
Microsoft Research Machine Learning and Perception Seminars
A complete set of rotationally and translationally invariant features based on a generalization of the bispectrum to non-commutative groups
Inference Group
Gaussian Process Product Models
Microsoft Research Machine Learning and Perception Seminars
Reverse Engineering the Human Visual System with Networks of Spiking Neurons
Machine Learning Reading Group @ CUED
An Introduction to Generalized Ensemble MCMC for Machine Learning
Inference Group
Metropolis-Coupled MCMC for Nested Sampling
Machine Learning Journal Club
Journal Club: "Analysis of Contour Motions"
Machine Learning @ CUED
Dirichlet Processes and Hierarchical Dirichlet Processes
⚠️ Advanced Machine Learning Tutorial Lecture
Machine Learning Reading Group @ CUED
Some NIPS papers
Inference Group
Some interesting data: Cryptic DNA sequence periodicities are ubiquitous, organism specific and distinguish introns from exons.
Machine Learning @ CUED
An Introduction to Non-parametric Bayesian Methods
⚠️ Room changed this week!
Machine Learning Reading Group @ CUED
Universal Artificial Intelligence, and Probability Monads
Machine Learning Reading Group @ CUED
Neural Networks
Inference Group
NIPS presentations
Machine Learning Journal Club
Journal Club: "Bayesian Policy Gradient Algorithms"
Microsoft Research Machine Learning and Perception Seminars
Margin- and Evidence-Based Approaches for EEG Signal Classification
Machine Learning Reading Group @ CUED
Latent Dirichlet Allocation and Dirichlet Diffusion Trees
Machine Learning Journal Club
Gaussian Process Belief Propagation
Machine Learning @ CUED
Robot Localisation and Mapping
⚠️ Advanced Machine Learning Tutorial Lecture
Machine Learning Reading Group @ CUED
Non-parametric mixture models
Machine Learning Journal Club
Loss Functions for Discriminative Training of Energy-Based Models
Inference Group
Modeling text with Dirichlet compound multinomial distributions
Machine Learning Journal Club
Estimation of non-normalized statistical models using score matching
Machine Learning Reading Group @ CUED
Collaborative Filtering
Inference Group
Learning from positive and unlabeled data
Inference Group
HMMs for controlling dynamical systems
Microsoft Research Machine Learning and Perception Seminars
Computer Go Research in the University of Alberta GAMES Group
Inference Group
Robust Gaussian Process Regression and Applications
Microsoft Research Machine Learning and Perception Seminars
Where’s my rocket? – Stochastic simulations of rocket flight paths
Machine Learning Journal Club
Journal Club: "Reducing the Dimensionality of Data with Neural Networks"
Inference Group
Transcription regulation: from parts list to genomic network.
Machine Learning Reading Group @ CUED
On Choosing Priors
Microsoft Research Machine Learning and Perception Seminars
Learning Conditional Random Fields with Hierarchical Features: Application to the Game of Go
Microsoft Research Machine Learning and Perception Seminars
Constraint Programming Techniques for Virtual Camera Control
Machine Learning Journal Club
Journal Club: "Clustering appearance and shape by learning jigsaws"
Machine Learning Reading Group @ CUED
Partially Observable Markov Decision Processes (POMDPs)
Inference Group
Bayesian estimation of neuronal connectivity from MEA recordings
Machine Learning Journal Club
Journal Club "Searching for memories, Sudoku, implicit check-bits, and the iterative use of not-always-correct rapid neural computation"
Machine Learning Reading Group @ CUED
Nonlinear Dimensionality Reduction
Inference Group
Vector Gaussian Processes
Machine Learning Journal Club
Journal Club: "Document language models, query models, and risk minimization for information retrieval"
Machine Learning Reading Group @ CUED
Machine Learning Reading Group in Engineering Department
Inference Group
Do Scanning Electron Microscopes have a point-spread function?
Microsoft Research Machine Learning and Perception Seminars
Learning microRNA regulatory networks from genomic sequence and expression data
Machine Learning Journal Club
Journal Club: A Neural Probabilistic Language Model
Machine Learning @ CUED
Gaussian Processes for Machine Learning
⚠️ Advanced Machine Learning Tutorial Lecture
Inference Group
Dynamic imaging of blood flow in the conjunctival and episcleral microcirculations of the human eye: a new chapter in diagnostic imaging?
Machine Learning Journal Club
Expectation Consistent Approximate Inference
Inference Group
Teaching Statistics - A Bag of Tricks
Inference Group
Leaf Selection for Maximising Diversity in a Tree
Machine Learning @ CUED
Mixture Models and the EM Algorithm
⚠️ Advanced Machine Learning Tutorial Lecture
Inference Group
Optimised Digital Fountain Codes for Medium Blocklengths
Inference Group
An interesting preprint
Inference Group
MCMC for doubly-intractable distributions
Machine Learning Journal Club
A Hierarchical Bayesian Language Model based on Pitman-Yor Processes
Inference Group
How to veto anonymously under surveillance?
Machine Learning Journal Club
Journal Club: "A Bayesian Analysis of Projective Incidence"
Inference Group
Naive OCR?
Inference Group
Cognitive Cars
Inference Group
neural phase codes
Inference Group
Graphical Combinator Programming
Inference Group
The Manufacture of Bioethanol - A brief Introduction
Inference Group
Divergence measures and latent variable models
Inference Group
Implicit learning in Music
Inference Group
On the Analysis of Ellipsoid Approximation of Nested Sampling
Inference Group
Gaussian Process models for solving ODEs
Computer Modelling in Biology
Ligand detection and discrimination by spatial relocalisation: the complexities of T cell activation
Inference Group
Music Access for the Disabled
Inference Group
Blissymbolics - The Emergence of a Written Language
Inference Group
In search of the brain's wiring
Inference Group
A Bayesian Unfolding Method Applied to the PAMELA Experiment
Inference Group
Blissymbolics
Machine Learning Journal Club
Journal Club: Dirty paper coding and applications
Inference Group
Finite element modelling of growing microtubules
Machine Learning Journal Club
Bayesian Sets
Machine Learning Journal Club
SUPERFAST SOLUTION OF REAL POSITIVE DEFINITE TOEPLITZ SYSTEMS
Inference Group
Title to be confirmed
Machine Learning Journal Club
Unsupervised Learning from Users' Error Correction in Speech Dictation
Machine Learning Journal Club
Hierarchical Bayesian inference in the visual cortex
Inference Group
How to Parameterize a Positive-definite Matrix
Inference Group
To be confirmed
Inference Group
The Capacity of the Deletion Channel
Machine Learning Journal Club
"Weak pairwise correlations imply strongly correlated network states in a neural population"
Machine Learning Journal Club
Machine Learning Journal Club: Gaussian Processes for Machine Learning Chapter VIII
Inference Group
Bayesian approximation for physical inverse problems
Inference Group
Hierarchical Passage Retrieval
Machine Learning Journal Club
Machine Learning Journal Club: "Gaussian Processes for Machine Learning"
Inference Group
Bayesian Online Changepoint Detection
Machine Learning Journal Club
Machine Learning Journal Club: "Gaussian Processes for Machine Learning"
Inference Group
Epitomic representation of natural signals
Machine Learning Journal Club
Machine Learning Journal Club: "Gaussian Processes for Machine Learning"
Inference Group
Aggregation of Particles in Stochastic Flows
Inference Group
Student Project Presentations: "Colour Memorisation", "One button communication" and "Optimising digital fountain codes"
Machine Learning @ CUED
Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model
Inference Group
Speech Recognition During Dictation Corrections
Machine Learning Journal Club
Machine Learning Journal Club: "Dependent Gaussian Processes"
Inference Group
Topic Modeling: Beyond Bag-of-Words
Inference Group
Uncertainty Logic
Machine Learning Journal Club
Machine Learning Journal Club: "Assessing Approximations for Gaussian Process Classification"
Inference Group
Experimental Annotation of the Human Genome
Machine Learning Journal Club
Machine Learning Journal Club: "Sparse Gaussian Processes using Pseudo-inputs"
Inference Group
Virtual-move Parallel Tempering
Machine Learning Journal Club
reading-group: Divergence measures and message passing
Inference Group
Latency-optimal fault-tolerant replication
Machine Learning Journal Club
reading-group: Expectation Propagation
Inference Group
Dynamical Networks for Agent Systems
Inference Group
Recognition and prediction of biological motion
Machine Learning Journal Club
reading-group: Interpolating Between Types and Tokens by Estimating Power-Law Generators
Inference Group
Eye Tracking with Consumer Hardware
Machine Learning Journal Club
reading-group: TD-Gammon
Inference Group
Cheap and cheerful inference machines for tracking
Machine Learning Journal Club
Reading Group: "Learning to Predict by the Methods of Temporal Differences"
Inference Group
Efficient Quantum Computation with linear optics
Inference Group
Locked-in syndrome
Inference Group
Monte Carlo is Bayesian
Inference Group
Dasher for the blind, and some visual illusions
Inference Group
A Cross-Entropy Based Method to Analyse Iterative Decoding
Inference Group
Bayesian Pattern Ranking for Move Prediction in the Game of Go
Inference Group
Adaptive Monte Carlo Methods for Simulation and Optimization
Inference Group
Language relationships, phonology, and Greek
Inference Group
Efficient communication with buttons: an update on Button Dasher
Inference Group
Progress with #Dasher, a Continuous Gesture IDE
Inference Group
Why do fruitflies like bananas?
Machine Learning Journal Club
multicanonical sampling
Inference Group
An introduction to Brain-Computer Interfaces
Inference Group
To be confirmed
Inference Group
LOCUS: Learning Object Classes with Unsupervised Segmentation
Inference Group
Efficient computer interfaces using continuous gestures, language models, and speech; Probabilistic methods for interpreting electronic ink
Inference Group
A Fast Learning Algorithm for Deep Belief Nets
Inference Group
Epitomes And Epitexts
Inference Group
Monte Carlo experiments in computer Go
Inference Group
Language Modelling In Dasher: A Tutorial
Inference Group
Hierarchical Dirichlet Process Models for Time Series Data
Machine Learning Journal Club
Reading group: Bayesian inference via search
Inference Group
Japanese Language Model
Inference Group
A Tale of T9
Inference Group
Information and Intelligence
Inference Group
Neighbourhood Components Analysis
Inference Group
Nested Sampling for Motif Discovery
Inference Group
Joint Gaussian Process-Density Mixtures
Inference Group
Computer Othello
Inference Group
Variational Gibbs Sampling
Inference Group
Astronomical Image Recognition
Inference Group
Bloom Filters and Tries (a comparison)
Inference Group
The predictive power of contractive neural-network spaces
Inference Group
Bayesian Learning for Visual Inference
Inference Group
Approximating The Hierarchical Dirichelet Distribution
Please see above for contact details for this list.