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Learning Theory and Statistical Optimization
Simon Vary edited this page Mar 7, 2025
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In these meetings, we invite speakers to present on a variety of topics addressing theoretical aspects of machine learning. These meetings are open to members of any department. For people who wish to attend remotely – please contact Simon for access to the Zoom call.
Organisers: Patrick Rebeschini, Varun Kanade, Simon Vary
This term, the meetings will take place from 16:15 - 17:15 on Thursdays of weeks 7, 8, and 9 in the Meeting Room 2 and the Open Research Area (ORA) of the Department of Statistics.
| Date | Location | Speaker | Title |
|---|---|---|---|
| 6/3 | ORA | Alex Buna-Marginean | First order methods for linearly separable data |
| 13/3 | Meeting Room 2 | Sam Howard | Schrödinger bridge solvers and connections to mirror descent |
| 20/3 | ORA | Jack Mayo | TBA |
| Date | Location | Speaker | Title |
|---|---|---|---|
| 23/10 | LG.04 | Arya Akhavan | Blackbox optimization in passive and active schemes |
| 06/11 | LG.04 | David Janz | Why and when randomised exploration works (in linear bandits) |
| 20/11 | LG.03 | Dorian Baudry | Improved learning rates in multi-unit uniform price auctions |
| 27/11 | LG.03 | Shirley Xiaoqi Liu | Loss landscapes and optimization in over-parameterized neural networks: an introduction |
| Date | Location | Speaker | Title |
|---|---|---|---|
| 13/06 | SLT | Johannes Müller | Geometry and Convergence of Natural Policy Gradients and Entropic Regularization |
| 04/06 | SLT | Sílvia Casacuberta Puig | Omniprediction: A new paradigm for loss minimization (paper) |
| 21/05 | SLT | Maria-Alexa Tudose | Online and learning-augmented algorithms |
| 30/04 | SLT | Eugenio Clerico | From coin betting to mean estimation and generalisation bounds |
| Date | Location | Speaker | Title |
|---|---|---|---|
| 07/03 | ORZ | Šimon Váry | Optimization without retraction on the random generalized Stiefel manifold |
| 29/02 | ORZ | Peter Potaptchik | Diffusion models in infinite dimensions |
| 22/02 | SLT | Emmeran Johnshon | The role of switching cost on minimax rates in online learning |
| 15/02 | SLT | Amitis Shidani | Information Theory, Generative Process and Contrastive Learning |
| Date | Speaker | Title |
|---|---|---|
| 01/12 | Charlie London | Generalization Bounds via Convex Analysis (paper) |
| 24/11 | Sam Howard | Optimal Transport, Sinkhorn's Algorithm and the Entropic Mapping Estimator (paper) |
| 17/11 | Carlo Alfano | Ordering-based Conditions for Global Convergence of Policy Gradient Methods (paper) |
| 10/11 | -- | -- |
| 03/11 | Alex Buna-Marginean | First Order Methods through Fenchel Games (paper) |
| Date | Speaker | Title |
|---|---|---|
| 05/05 | Kaja Gruntkowska | Distributed Optimisation with Bidirectional Compression |
| 19/05 | Shahine Bouabid | Learning with Kernels |
| 16/06 | Alireza Amanihamedani | Decentralized Online Learning |
| Date | Speaker | Title |
|---|---|---|
| 27/01 | Alex Buna-Marginean | First order methods in robustness |
| 10/02 | Group lunch | No talk due to COLT deadline |
| 10/03 | Emmeran Johnson | Minimax lower bounds for policy evaluation in reinforcement learning |
| Date | Speaker | Title |
|---|---|---|
| 28/10 | Carlo Alfano | Reinforcement learning: Theory of policy gradient methods |
| 11/11 | Eugenio Clerico | Transport inequalities and large deviations |
| 25/11 | Amitis Shidani | Bandit learning: Theory of linear contextual setting |
| 09/12 | Tyler Farghly | The interface between sampling and optimization: functional inequalities and Langevin Monte Carlo |
| Week | Speaker | Title |
|---|---|---|
| 2 | Amartya Sanyal | Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak- Lojasiewicz Condition |
| 3 | Gonzalo Mena | Statistical Bounds for Entropic Optimal Transport and Sinkhorn EM |
| 4 | Eugenio Clerico | Some Information-Theoretic Generalization Bounds |
| 5 | Eduard Oravkin | The Double Descent Phenomenon and Implicit Bias of Mirror Descent |
| 6 | David Martinez | Accelerating Variance-Reduced Stochastic Gradient Methods |
| 7 | --- | --- |
| 8 | Jun Yang | Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm |
| Week | Speaker | Title |
|---|---|---|
| 2 | Dominic Richards | Learning with Gradient Descent and Weakly Convex Losses (with M. Rabbat) |
| 3 | --- | --- |
| 4 | Valentin De Bortoli | Quantitative Propagation of Chaos for SGD in Wide Neural Networks |
| 5 | Fan Wu | Minimax Rates of Estimation for Sparse PCA in High Dimensions |
| 6 | Carlo Alfano | On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift |
| 7 | Tyler Farghly | A Non-Asymptotic Analysis of Stochastic Gradient Langevin Dynamics |
| 8 | Tomas Vaskevicius | Fast Rates and Sparse Linear Prediction |
For notes and more information, refer to the old github page.