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Hugues Van Assel; Γcole Normale SupΓ©rieure de Lyon.
Thursday 13 June 2024, 11:00-12:00
Contact: 41755
0 upcoming talks View 425 archived talks
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 @ CUED
Consistent Validation for Predictive Methods in Spatial Settings
Machine Learning @ CUED
Modern Bayesian Experimental Design
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 @ CUED
Convergence bounds for the Random Walk Metropolis algorithm - Perspectives from Isoperimetry
Machine Learning @ CUED
Calls to F the Algorithm: Lessons from the 2020 Exam Debacle
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 @ CUED
Gaussian processes, spectral analysis kernels and optimal transport
Machine Learning @ CUED
Deep Reinforcement Learning for Multi-Agent Interaction
Machine Learning @ CUED
Presenting Hawk-Eyeβs 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 @ CUED
Noise-Aware Differentially Private Synthetic Data
Machine Learning @ CUED
Estimating RSV seasonality from pandemic disruptions: a modelling study
Machine Learning @ CUED
Beyond Conformal Prediction: Distribution-Free Uncertainty Quantification for Complex Machine Learning Tasks
Machine Learning @ CUED
Graph neural network approach for decentralized multi-robot coordination
Machine Learning @ CUED
Efficient and Structured Uncertainty: Challenges and Opportunities
Machine Learning @ CUED
Game Playing Meets Game Theory: Strategic Learning from Simulated Play
Machine Learning @ CUED
Robust Deep Learning Under Distribution Shift
Machine Learning @ CUED
Models and inference for temporal Gaussian processes
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 @ 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 @ CUED
Nonparametric Generative Modeling via Optimal Transport and Diffusions with Provable Guarantees
Machine Learning @ CUED
NeVAE: A Deep Generative Model for Molecular Graphs
Machine Learning @ CUED
Global model explainability via aggregation
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
Generative models for few-shot prediction tasks
Machine Learning @ CUED
Online Meta-Learning
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 @ 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 @ 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 @ CUED
Structure in tensor-variate data: a trivial byproduct of simpler phenomena?
Machine Learning @ CUED
Lipschitz Global Optimization
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 @ 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 @ CUED
Towards true end-to-end learning & optimization
Machine Learning @ CUED
Targeted Disclosure to Support Auditing and Accountability for Automated Decision-making
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
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 @ 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 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 @ 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 @ CUED
Learning with limited supervision
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 @ CUED
Probabilistic Numerical Computation: A New Concept?
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.
β οΈ Note venue: James Dyson Building
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
β οΈ Note: via Skype!
Machine Learning @ CUED
Automated Reasoning and AI for Large Formal Mathematics
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 @ CUED
Sum-Product Networks for Probabilistic Modeling
Machine Learning @ CUED
MLE-Struct: Bethe Learning of Graphical Models
Machine Learning @ CUED
Inference and Learning in the Anglican Probabilistic Programming System
Machine Learning @ CUED
Unsupervised Risk Estimation with only Structural Assumptions
Machine Learning @ CUED
The geometry of uncertainty
Machine Learning @ CUED
A more Automated Statistician
Machine Learning @ CUED
General Reinforcement Learning
Machine Learning @ CUED
Inference of a partially observed kinetic Ising model
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
β οΈ (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
Modeling Confounding by Half-Sibling Regression
β οΈ SHORT TALK (20 minutes)
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.
β οΈ This talk will be given via Skype
Machine Learning @ CUED
Do Deep Nets Really Need to be Deep?
Machine Learning @ CUED
Probabilistic numerics: treating numerical computation as learning, or; it's Bayes all the way down
Machine Learning @ CUED
Machine Learning for Quantitative Finance: A collaboration between the Cambridge Machine Learning Group and Cambridge Capital Management
Cambridge Society for the Application of Research (CSAR)
New Developments in Machine Intelligence
β οΈ This talk will be preceeded by a short presentation from Laura Burzynski on her research into blood clotting proteins
Rouse Ball Lectures
Mathematics of Crime
Machine Learning @ CUED
Title to be confirmed
Machine Learning @ CUED
Inference for infinite mixture models and Gaussian Process mixtures of experts using simple approximate MAP Inference
Machine Learning @ CUED
Evolutionary dynamics in a continuous public goods game
Microsoft Research Cambridge, public talks
Social Media Predictive Analytics: Methods and Applications
β οΈ 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 introduction to the Mondrian Process
Machine Learning @ CUED
Population Inference for Functional Brain Connectivity
Machine Learning @ CUED
Probabilistic approaches to understanding bird conversations
Machine Learning @ CUED
A* Sampling
Machine Learning @ CUED
Orthologous networks in biological systems
Machine Learning @ CUED
Latent Branching Trees
Machine Learning @ CUED
Experiments with Non-parametric Topic Models
Machine Learning @ CUED
The Blended Paradigm: A Bayesian Approach to Handling Outliers and Misspecified Models
Machine Learning @ CUED
Machine Translation with LSTMs
β οΈ Room changed to LR3B in Inglis building and time changed to 10:30
Machine Learning @ CUED
Oracle Variational Inference
Machine Learning @ CUED
Bayesian modeling for high-level real nursing activity recognition using accelerometers
β οΈ short talk
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 Ο-Stable Poisson-Kingman mixture models
Machine Learning @ CUED
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
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 @ 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?
β οΈ NOTE THE SPECIAL VENUE (Microsoft Research)
Machine Learning @ CUED
Practical Machine Learning at Facebook. Examples and Lessons Learnt.
β οΈ Room changed to LR4
Machine Learning @ CUED
Bayesian inference for integer-valued LΓ©vy processes with Non-Gaussian Ornstein-Uhlenbeck volatility modelling
β οΈ short talk (30 mins) - NOTE TIME CHANGE
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 @ CUED
Probabilistic computing applications: BayesDB and stochastic digital circuits
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.
β οΈ Note: time is 12 noon (*not* 11am)
Machine Learning @ CUED
Bayesian canonical correlation analysis
Machine Learning @ CUED
Cambridge - Tuebingen PhD Applicant Talks (via Skype)
β οΈ Interview talks via Skype
Machine Learning @ CUED
Generative probabilistic programming: applications and new ideas
Machine Learning @ CUED
Bayesian nonparametrics: Dependency and Constraint Modeling
β οΈ 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
β οΈ Note venue: LR6
Machine Learning @ CUED
Frequentist coverage of adaptive nonparametric Bayesian credible sets
Machine Learning @ CUED
Anglican; Particle MCMC inference for Probabilistic Programs
Machine Learning @ CUED
Sparse discriminative latent characteristics for predicting cancer drug sensitivity
Machine Learning @ CUED
Contrastive Learning Using Spectral Methods
Machine Learning @ CUED
Nonparametric Bayesian statistics with exchangeable random structures
Machine Learning @ CUED
Dissecting genotype to phenotype relationships
Machine Learning @ CUED
CANCELLED: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
β οΈ CANCELLED
Machine Learning @ CUED
Clustering Based on Predictive Variances in Gaussian Process Regression Models
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
Machine Learning @ CUED
Annealing Between Distributions by Averaging Moments
NLIP Seminar Series
Recursive Deep Learning for Modeling Semantic Compositionality
Machine Learning @ CUED
Applied statistical genetics and next-generation association studies
Machine Learning @ CUED
Applied statistical genetics and next-generation association studies
Machine Learning @ CUED
The combinatorial structure of conditionally i.i.d. negative binomial processes directed by a beta process
jg691's list
Google's Approach to Building Relationships with Universities: Presentation and Talk by Dr David J Harper
Machine Learning @ CUED
Matrix Concentration Inequalities via the Method of Exchangeable Pairs
Machine Learning @ CUED
Deep Gaussian Processes
Machine Learning @ CUED
Approaches to statistical modeling of network data
Machine Learning @ CUED
Bayesian nonparametric methods for non-exchangeable data
Machine Learning @ CUED
Non-parametric Bayesian Chromatin State Segmentation
Machine Learning @ CUED
Fast Variational Inference in the Conjugate Exponential Family
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 @ CUED
Feature allocations, probability functions, and paintboxes
Machine Learning @ CUED
Modelling Reciprocating Relationships with Hawkes Processes
Machine Learning @ CUED
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables
Machine Learning @ CUED
The combinatorial structure underlying a beta processes is that of a continuum of Blackwell-MacQueen urn schemes
Machine Learning @ CUED
Learning of Milky Way Model Parameters Using Matrix-variate Data in a New Gaussian Process-based Method
Machine Learning @ CUED
Identification of causal effects
Machine Learning @ CUED
Probabilistic methods for biomolecular structure simulations
Computer Laboratory Systems Research Group Seminar
Distributed, Real-Time Bayesian Learning in Online Services
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
β οΈ 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
Machine Learning @ CUED
Frank-Wolfe optimization insights in machine learning
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
Machine Learning @ CUED
Human Behavior Classification with Infinite Hidden Conditional Random Fields
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
Machine Learning @ CUED
Discovery of Complex Behaviors through Contact-Invariant Optimization
Machine Learning @ CUED
Learning with nonparametric dependence and divergence estimation
Machine Learning @ CUED
Past work and future interests: respectively, the scattering of Anyons and Monte Carlo methods
Machine Learning @ CUED
Non-parametric Bayesian Method and Maximum-A-Posteriori Inference in Statistical Machine Translation
Machine Learning @ CUED
Infinite Structured Explicit Duration Hidden Markov Models
Machine Learning @ CUED
Probabilistic computing: computation as universal stochastic inference, not deterministic calculation
Machine Learning @ CUED
Scaling Machine Learning for the Internet
Machine Learning @ CUED
Bayesian Quadrature for Prediction and Optimisation
Machine Learning @ CUED
Efficient MCMC for Continuous Time Discrete State Systems
Machine Learning @ CUED
A Maximum Entropy Perspective on Spectral Dimensionality Reduction
Machine Learning @ CUED
Variational Inference for Non-Conjugate Models
Machine Learning @ CUED
Not so naive Bayesian classification
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 PΓ³lya Urns and their applications
Machine Learning @ CUED
Approximate Bayesian Inference for Large Scale Inverse Problems: A Computational Viewpoint
Machine Learning @ CUED
Some Practical Reflections on Graphical Models
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
β οΈ (rescheduled from May 24th)
Machine Learning @ CUED
Censored Exploration in Dark Pools
Machine Learning @ CUED
An FX trading system using adaptive reinforcement learning
β οΈ RESCHEDULED FOR JUNE 7th
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
Machine Learning @ CUED
Nonlinear Dynamics of Learning
Machine Learning @ CUED
Exponential Conditional Volatility Models
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 @ CUED
Title to be confirmed
Machine Learning @ CUED
Differential Geometric MCMC Methods
Machine Learning @ CUED
Mining viral datasets
Machine Learning @ CUED
Universal Bayesian Agents: Theory and Applications
Machine Learning @ CUED
Machine learning in cancer research (a.k.a CRI meets CUED)
β οΈ (note new date)
Machine Learning @ CUED
Novi Quadrianto
Machine Learning @ CUED
Learning item trees for collaborative filtering with implicit feedback
Machine Learning @ CUED
Non-Smooth-Norm Image Reconstruction from Noisy Data
Machine Learning @ CUED
Crowdsourcing data modelling
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 @ CUED
Searching for Knowledge Instead of Web Pages
Machine Learning @ CUED
Bayesian Inference with Kernels
Machine Learning @ CUED
On the Equivalence of Graph Cuts and Max-product Belief Propagation
Machine Learning @ CUED
Culture wars, voting and polarization: divisions and unities in modern American politics
β οΈ general audience talk (non-technical) -- NOTE change of room to LR4!
Machine Learning @ CUED
Creating structured and ο¬exible models: some open problems
Machine Learning @ CUED
Efficient Bayesian analysis of multiple changepoint models
Machine Learning @ CUED
Learning Common Grammar from Multilingual Corpus / Online Multiscale Dynamic Topic Models
Machine Learning @ CUED
Scalable Parallel Computing with CUDA
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
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 @ CUED
Structured Prediction Cascades
Machine Learning @ CUED
Parametric Bandits, Query Learning, and the Haystack Dimension
Machine Learning @ CUED
Slice sampling with latent Gaussian models
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
β οΈ short talk
Machine Learning @ CUED
Bayesian Inference in Networks of Queues
Machine Learning @ CUED
Stochastic Outlier Selection
Machine Learning @ CUED
Mind Reading by Machine Learning: Optimal Experimental Design
β οΈ Short talk
Machine Learning @ CUED
Machine Learning Course (4F13)
β οΈ 4F13 course - repeats Wed, Thurs at 10am during Lent
Machine Learning @ CUED
Machine Learning Course (4F13)
β οΈ 4F13 course - repeats Wed, Thurs at 10am during Lent
Machine Learning @ CUED
Indian Buffet Processes with Power-law Behaviour
Machine Learning @ CUED
Gaussian Processes for Active Data Selection, Faults, Changepoints and Sensor Selection
Machine Learning @ CUED
KL control theory and decision making under uncertainty
Machine Learning @ CUED
Information theoretic model selection in clustering
Machine Learning @ CUED
Motor Skills Learning for Robotics
Machine Learning @ CUED
Coconut: Optimizing computations for machine learning
Machine Learning @ CUED
Convex Variational Bayesian Inference for Large Scale Generalized Linear Models
Machine Learning @ CUED
Computable Probability Theory
Machine Learning @ CUED
Using gradient descent for optimization and learning
Machine Learning @ CUED
Learning from Measurements in Exponential Families
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 @ CUED
Generalization in Learning
Machine Learning @ CUED
Stochastic control as an inference problem
Machine Learning @ CUED
Extending the Affinity Propagation Model
Machine Learning @ CUED
The Block Diagonal Infinite Hidden Markov Model
Machine Learning @ CUED
Probabilistic Graph Models for Debugging Software
Machine Learning @ CUED
HMMs for Protein Sequencing from Mass Spectrometry Data
Machine Learning @ CUED
Mondrian Processes
Machine Learning @ CUED
Hardness Ratios
Machine Learning @ CUED
Consensus finding, exponential models and infinite rankings
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part III)
Machine Learning @ CUED
Efficient Sequential Monte Carlo Inference for Kingman's Coalescent
β οΈ Note unusual time
Machine Learning @ CUED
Context in human robot interaction
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part II)
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 @ CUED
Spoken Dialogue Management
Machine Learning @ CUED
A Bayesian approach to language learning
Machine Learning @ CUED
Learning Bigrams from Unigrams
Machine Learning @ CUED
Nonparametric Bayesian Natural Language Model Domain Adaptation: A Hierarchical, Hierarchical Pitman-Yor Process Language Model
Machine Learning @ CUED
Bayesian approaches to autonomous Bayesian real-time learning
Machine Learning @ CUED
Non-negative matrix factorization with Gaussian process priors
Machine Learning @ CUED
Matrix Factorization and Relational Learning
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 @ CUED
Bayesian analysis of complex biological systems
Machine Learning @ CUED
Variational inference for partially observed diffusion processes
Machine Learning @ CUED
Assessing high-dimensional latent variable models
Machine Learning @ CUED
Identification of dynamical subpopulations, optimal experiment design and more
Machine Learning @ CUED
H-Infinity Clustering
Machine Learning @ CUED
Sustainable Energy - without the hot air
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 @ CUED
An Introduction to Statistical Learning Theory
β οΈ Machine Learning Tutorial Lecture
Machine Learning @ CUED
Beam Sampling for Infinite Hidden Markov Models
Machine Learning @ CUED
Learning quantum physics
Machine Learning @ CUED
Convergence analysis of the EM algorithm and joint minimization of free energy
Machine Learning @ CUED
Discriminative Methods with Structure
Machine Learning @ CUED
A Bayesian approach to network modularity: inferring the structure and scale of modular networks
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 @ CUED
Stable distribution and data sketching
Machine Learning @ CUED
Reinforcement Learning
β οΈ Machine Learning Tutorial Lecture (Note unusual day)
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 @ CUED
Gene Regulatory Network Inference: A Kernel-Based Learning Approach
Machine Learning @ CUED
Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
Machine Learning @ CUED
Machine Learning Applications / Challenges in Natural Language Parsing
β οΈ Machine Learning Tutorial Lecture
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 @ CUED
Hidden Common Cause Relations in Relational Learning
Machine Learning @ CUED
Optimal Spreading Sequences for Chaos-Based Communication Systems; Using CSK as a Case Study
Machine Learning @ CUED
Graph Kernels for Data Mining
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
Machine Learning @ CUED
Logistic Regression with a Laplacian prior on the Eigenvalues: Convex duality and application to EEG classification
β οΈ Note different room and time
Machine Learning @ CUED
Dirichlet Processes and Hierarchical Dirichlet Processes
β οΈ Advanced Machine Learning Tutorial Lecture
Machine Learning @ CUED
An Introduction to Non-parametric Bayesian Methods
β οΈ Room changed this week!
Machine Learning @ CUED
Robot Localisation and Mapping
β οΈ Advanced Machine Learning Tutorial Lecture
Machine Learning @ CUED
Gaussian Processes for Machine Learning
β οΈ Advanced Machine Learning Tutorial Lecture
Machine Learning @ CUED
Mixture Models and the EM Algorithm
β οΈ Advanced Machine Learning Tutorial Lecture
Machine Learning @ CUED
Probabilistic Dimensional Reduction with the Gaussian Process Latent Variable Model
Please see above for contact details for this list.