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MR5, CMS Pavilion A
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If you have a question about this list, please contact: . If you have a question about a specific talk, click on that talk to find its organiser. 4 upcoming talks and 31 talks in the archive. Feature learning and normalization layers
Quantum Machine Learning: An Information-Theoretic Perspective
Information-theoretic techniques and context-tree methods for time series
A theory of social balance
Enabling Fast, Robust, and Personalized Federated Learning
Convexity properties of information functionals for Gaussian mixtures
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
Classical and quantum zero error communication
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Analysis
Sharp convergence guarantees for iterative (non)-convex empirical risk minimization with random data
Load Balancing under Data Locality: Extending Mean-Field Framework to Constrained Large-Scale Systems
The multicast transmission approach to converse theoremsNote unusual day of the week
Causal Identification: Are We There Yet?
Error exponents for source coding under logarithmic loss
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
Uniform entropic continuity bounds via majorization flow
How the strength of the inductive bias affects the generalization performance of interpolators
Typical error exponents
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
Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities
Strong and epsilon-dependent converses for source or channel coding and for hypothesis testing
Please see above for contact details for this list. |
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Dr Matus Telgarsky, NYU.
Wednesday 13 March 2024, 14:00-15:00