Prof. Ramji Venkataramanan

Name:	Prof. Ramji Venkataramanan
Affiliation:	University of Cambridge
E-mail:	(only provided to users who are logged into talks.cam)
Last login:	14 Apr 2026, 8:52 a.m.

Public lists managed by Prof. Ramji Venkataramanan

• Communications Research Group Seminar
• Information Engineering Distinguished Lecture Series
• Information Theory Seminar
• Machine learning
• Probabilistic Systems, Information, and Inference Group Seminars

Talks given by Prof. Ramji Venkataramanan

Obviously this only lists talks that are listed through talks.cam. Furthermore, this facility only works if the speaker's e-mail was specified in a talk. Most talks have not done this.

• Bayes-optimal estimation in generalized linear models
• Approximate Message Passing Algorithms
• Capacity-achieving codes for the AWGN channel via approximate message passing decoding
• Low-complexity codes for Compression and Communication via Sparse Regression

Talks organised by Prof. Ramji Venkataramanan

This list is based on what was entered into the 'organiser' field in a talk. It may not mean that Prof. Ramji Venkataramanan actually organised the talk, they may have been responsible only for entering the talk into the talks.cam system.

• Group Entropies: functionals on probability spaces, state space growth rates, energy, and connections to thermodynamics
• Towards a Theoretical Understanding of Deep Learning via the Minimum Description Length Principle
• The Minimum Description Length Principle and Machine Learning
• Information Constraints in Human Decision-Making: From Theory to Computation
• Statistical Investigations into the Unseen: Missing Mass for Markov Samples and Natural Distribution Estimation
• Variational Inference for Lévy Process-Driven SDEs via Neural Tilting
• Zero-Error Communications over Modulo-Additive Noise Channels with Assistance
• De Bruijn's identity and stability in the Entropy Power Inequality without finite variance
• Statistical Signal Processing for Quantum Error Mitigation
• Efficient gradient coding for mitigating stragglers within distributed machine learning
• Inequalities Revisited
• Inequalities Revisited
• Latent Concepts in Large Language Models
• Learning Privately in High Dimensions
• Cambridge Information Theory Colloquium
• Causal Representation Learning
• Graph Data Compression: Practical Methods and Information-Theoretic Limits
• Group Testing: Something old, something new, something borrowed
• Entropy comparison inequalities with applications to additive noise channels
• Data Compression with Relative Entropy Coding
• Can kernel machines be a viable alternative to deep neural networks?
• Sampling using Diffusion Processes
• Time-varying Signal Estimation Using Dynamic Topological Graphs
• Generalization and Informativeness of Conformal Prediction
• From Classical to Quantum: Uniform Continuity Bounds on Entropies in Infinite Dimensions
• A State-Space Perspective on Modelling and Inference for Online Skill Rating
• Symmetrized KL information: Channel Capacity and Learning Theory
• Structural, Temporal and Semantic Information
• Different Target Prediction Algorithms for Automotive, HRI and VR Digital Twin
• Communication over many-user channels via Approximate Message Passing
• Quickest Change Detection Using Mismatched CUSUM
• Information Theory Colloquium
• Some combinatorial problems in information theory
• Quantum Machine Learning: An Information-Theoretic Perspective
• Information-theoretic techniques and context-tree methods for time series
• A theory of social balance
• The Role of Information Measures on the Regularization of Empirical Risk Minimization
• Provably Safe Certification for Machine Learning Models under Adversarial Attacks
• Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression
• A functional perspective on Information Measures
• AI for Sound
• AI for Sound
• Causal Identification: Are We There Yet?
• Approximate fixed points of classical and quantum channels and robustness theory of quantum Markov chains
• Fundamental limits in structured PCA, and how to reach them
• Generalization Bounds via Online Learning
• Discriminating Classical and Quantum Channels
• Semantic and Pragmatic Communications
• Bayes-optimal estimation in generalized linear models
• An Entropy-Based Model for Hierarchical Learning
• How the strength of the inductive bias affects the generalization performance of interpolators
• Communication in the presence of adversarial jamming: The curious case of the erasure channel
• Energy-Optimal Signaling using the Example of Optical Communication
• On the monotonicity of entropy in the discrete entropic central limit theorem
• Title to be confirmed
• Strong and epsilon-dependent converses for source or channel coding and for hypothesis testing
• Fundamental Limits of Learning with Feedforward and Recurrent Neural Networks
• Computational Imaging and Sensing: Theory and Applications
• A mathematical theory of deep neural networks
• Challenges and Opportunities in Computational Imaging and Sensing
• On the Stability and the Uniform Propagation of Chaos Properties of Ensemble Kalman-Bucy Filters
• Reliability function near the zero error capacity
• Lie Group Machine Learning and Natural Gradient from Information Geometry
• Gradient-based Adaptive Markov Chain Monte Carlo
• Random Caching Based Transmission in Ultra-Dense Wireless Networks
• Simple Reinforcement Learning Algorithms for Continuous State and Action Space Systems
• A local weak limit approach to the study of graphical data
• The Statistical Finite Element Method
• Deep Learning for Multifarious Speech Processing: Tackling Multiple Speakers, Microphones, and Languages
• Deep Learning for Multifarious Speech Processing: Tackling Multiple Speakers, Microphones, and Languages
• Can machine learning trump theory in communication system design?
• Symmetry, bifurcation, and multi-agent decision-making
• Deep Reinforcement Learning: from AlphaGo to AlphaStar
• Unleashing your inner maker
• Latent Variable Models for Bayesian Inference with Stable Distributions and Processes
• On optimal sampling in off-the-grid sparse regularisation.
• Estimating low-rank matrices via approximate message passing
• Fastest Convergence for Reinforcement Learning
• Design techniques for sparse regression codes
• Geometric MCMC for infinite-dimensional inverse problems
• Active Machine Learning: From Theory to Practice
• Information-theoretic perspectives on learning algorithms
• Measurement-dependent Noisy Search: An Information Acquisition Approach
• Biological Systems as Communication Networks
• Bilinear Inverse Problems: How much does structure help? (Leverhulme Lecture)
• Models and Capacity Bounds for Optical Fibre Channels
• High Resolution Optical and SAR Satellite Image Processing for Disaster Management using Hierarchical MRFs
• Tracking intentionality using behavioural models and Bayesian methods.
• Multi-band Image Super-resolution
• Algorithms and bounds for group testing
• Cluster-Seeking Shrinkage Estimators
• Exploiting Asymptotic Structure for Resolution Enhancement in Physical Imaging Systems
• Shrinkage Estimation in High Dimensions
• Adaptive Group Sparsity Using the Ordered Weighted l1 Regularizer
• Approximate Smoothing and Parameter Estimation in High-Dimensional State-Space Models
• Scalable inference for a full multivariate stochastic volatility model
• Ocean Heat and Hot air (with apologies to David MacKay)
• Cloud Radio Access Networks: Challenges and (Some) Solutions
• Gain from Multiple Measurements: Measurement Diversity and Resource Allocation
• Non-stationary Network Modelling by Particle Filtering: case of Gene Interaction Networks
• Capturing Accurate Colour Images
• Construction of Capacity-Achieving Lattice Codes: Polar Lattices
• A Novel Application of Information Theory in Heart Sound Signal Analysis for Cardiovascular Disease Diagnosis
• Characterizing degrees of freedom through additive combinatorics
• Eye Gaze Tracking
• Bayesian estimation for multi-object systems
• PREMIER: PRobabilistic Error-correction using Markov Inference in Errored Reads
• Online Robust Principal Components Analysis
• Factor Graph Transforms
• Multilevel sequential Monte Carlo Samplers.
• Recent Advances in Information Processing
• Inferring Cancer Evolution
• Using random linear network coding in dynamic storage environments
• Sequential Monte Carlo for graphical models: Graph decompositions and Divide-and-Conquer SMC
• The application of compressed sensing for longitudinal MRI
• Title to be confirmed
• Tracking the odd: Meter inference in musical audio using particle filters
• Compressible priors for high-dimensional statistics
• SCATTERING CONVOLUTION NETWORKS and DUAL-TREE WAVELETS
• On designing robust cost functions – applications to pattern analysis
• Title to be confirmed
• Radio Aspects of the Home Area Network for Smart Metering
• Dual-to-kernel learning with ideals
• Multiple Description Coding
• Wireless communication in electromagnetic cavities
• Classification on the Grassmann Manifold: Performance Limits of Compressive Classifiers
• Is the Gaussian distribution "Normal"?: Signal processing with alpha-stable distributions
• Joint Source-Channel Coding with Fading Channel and Side Information
• INDEPENDENT COMPONENT ANALYSIS VIA NONPARAMETRIC MAXIMUM LIKELIHOOD ESTIMATION
• Dynamic State Estimation using Dirac Mixture Approximation and Directional Statistics
• Communication by Statistical Regression
• Understanding Audio and Video at Google
• Bandlimited Intensity Modulation
• Estimation with Incomplete State Information in the Smart Grid
• Energy-efficient wireless communications
• Probabilistic modelling of time-frequency representations with application to music signals
• Interactive Codes for File Synchronization
• On the convergence of Adaptive sequential Monte Carlo Methods
• Demodulation and time-frequency analysis as inference
• Low-complexity codes for Compression and Communication via Sparse Regression
• High-SNR Asymptotics of Mutual Information for Discrete Constellations