Talks schedule

Year 2024

Apr 26, 13:00	Eliott Paquette	Random matrix theory for high dimensional optimization, and an application to scaling laws	Microsoft Research Labs
Apr 17, 13:00	Gauthier Gidel	On the stability of iterative retraining of generative models on their own data	Microsoft Research Labs
Apr 03, 13:00	Adam Oberman	Theoretical insights into self-supervised feature representation learning.	Microsoft Research Labs
Mar 20, 13:00	Jose Gallego-Posada	On PI controllers for updating Lagrange multipliers in constrained optimization	Microsoft Research Labs
Mar 06, 13:00	Motahareh Sohrabi	Weight-Sharing Regularization	Mila, Auditorium 2
Feb 21, 13:00	Louis Fournier	Can Forward Gradient Match Backpropagation?	Mila, Auditorium 2
Feb 09, 13:00	Michael Rabbat	Benchmarking Neural Network Training Algorithms	Microsoft Research Labs

Year 2023

Dec 07, 13:00	Juan Ramirez	Mitigating the Disparate Impact of Pruning with Constrained Optimization	Mila, Agora
Nov 16, 13:00	Charles Guille-Escuret	No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths	Mila, Agora
Oct 19, 13:30	Courtney Paquette	Hitting the High-D(imensional) Notes: An ODE for SGD learning dynamics	Microsoft Research Labs
Jul 19, 13:00	Bonaventure Dossou	Bridging Linguistic Frontiers: Machine Learning & NLP Innovations Empowering African Languages: Challenges, Progress, and Promising Futures	Auditorium 1
Jul 12, 13:30	Tiffany Vlaar	Constrained and Multirate Training of Neural Networks	Auditorium 1
Jul 05, 13:30	Guillaume Huguet	The heat operator for dimensionality reduction and optimal transport	Auditorium 1
Jun 28, 13:30	Lyle Kim	Adaptive Federated Learning with Auto-Tuned Clients	Auditorium 1
Jun 14, 13:30	Nicolas Le roux	Recent advances in functional optimization	Auditorium 2
May 31, 13:30	Tristan Deleu	Causal discovery with GFlowNets	Auditorium 2
May 03, 13:30	Sébastien Lachapelle	Discovering Latent Structures from Data	Auditorium 1
Apr 19, 13:30	Arna Gosh	alpha-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay	Mila, Agora
Mar 29, 13:30	Alexia Jolicoeur-Martineau	PopulAtion Parameter Averaging (PAPA)	Auditorium 1
Mar 15, 13:30	Alex Tong	Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport	Auditorium 1
Feb 14, 10:30	Marwa El Halabi	Difference of submodular minimization via DC programming	Auditorium 2

Year 2021

Jul 14, 16:00	Mark Sellke	A Universal Law of Robustness via Isoperimetry	Room H.07
Jun 16, 16:00	Jelena Diakonikolas	Structure in Min-Max Optimization (and How to Use It!)	Room H.07
Jun 02, 16:00	Tong Zhang	Why Cosine Learning Rate Scheduler Works and How to Improve It	Room H.07
May 30, 16:00	Ludwig Schmidt	Evaluating Machine (Human) Accuracy and Robustness on ImageNet	Room H.07
May 21, 16:00	Lenka Zdeborova	Insights on Gradient-based algorithms in high-dimensional non-convex optimisation	Room H.07
May 07, 16:00	Aaron Defazio	Why are we still using SGD in the 21st century? Adventures in large-scale deep learning	Room H.07
May 05, 16:00	Vasilis Syrgkanis	Adversarial machine learning and instrumental variables for flexible causal modeling	Room H.07
Mar 24, 16:00	Sebastien Bubeck	A law of robustness for two-layers neural networks	Room H.07
Mar 10, 16:00	Margarida Carvalho	Combinatorial optimization for games	Room H.07
Feb 24, 16:00	Anastasios Kyrillidis	Distributed learning of deep neural networks using independent subnet training	Room H.07
Feb 10, 16:00	Lorenzo Rosasco	An implicit tour of regularization	Room H.07
Jan 27, 16:00	Robert M. Gower	New Viewpoints, Variants and Convergence Theory for Stochastic Polyak Stepsizes	Room H.07
Jan 13, 16:00	Panayotis Mertikopoulos	Dynamics, (min-max) optimization, and games	Room H.07

Year 2020

Dec 02, 16:00	Nicolas Loizou	SGD for Modern Machine Learning, Practical Variants and Convergence Guarantees	Virtual
Nov 04, 16:00	Ashia Wilson	Variational Perspectives on Machine Learning, Algorithms, Inference, and Fairness	Virtual
Oct 07, 16:00	Karolina Dziugaite	Distribution-dependent generalization bounds for noisy, iterative learning algorithms	Virtual
Sep 23, 16:00	Aude Genevay	Learning with entropy-regularized optimal transport	Virtual
Sep 09, 16:00	Geoffrey Negiar	Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization	Virtual
Aug 26, 16:00	Hanie Sedghi	What is being transferred in transfer learning?	Virtual
Aug 12, 16:00	Kamalika Chaudhuri	Challenges in Reliable Machine Learning	Virtual
Aug 07, 16:00	Rachel Ward	Weighted Optimization, better generalization by smoother interpolation	Virtual
Jul 31, 16:00	Costis Daskalakis	The Complexity of Min-Max Optimization	Virtual
Jul 03, 16:00	Rachael Tappenden	Accelerated Gradient Methods with Optimality Certificates	Virtual
Jun 05, 16:00	Francis Bach	On the effectiveness of Richardson Extrapolation in Machine Learning	Virtual
May 22, 16:00	Tim Hoheisel	Cone-Convexity and Composite Functions	Virtual
May 08, 16:00	Peter Richtarik	On Second Order Methods and Randomness	Virtual
May 01, 16:00	Adam Oberman	Accelerated stochastic gradient descent, convergence rate and empirical results	Virtual
Apr 17, 16:00	Mark Schmidt	Faster Algorithms for Deep Learning?	Virtual