Robust Optimization and Generalization
Explore robust optimization's history and its modern machine learning applications, focusing on performance certification and practical challenges in ML systems.
Weak Limits for Entropic Optimal Transport - Part II
Explore weak limits for entropic optimal transport, covering parametric cases, duality, proofs, and connections to improved CLT and convex optimization.
Mathematical Problems in Machine Learning - Lecture 2/4
Explore mathematical challenges in machine learning, focusing on advanced concepts and problem-solving techniques in this comprehensive lecture series.
Online Optimization Meets Federated Learning - Tutorial
Explore state-of-the-art theoretical results in online optimization, federated learning, and their intersection. Gain insights into regret analysis, data heterogeneity, and distributed online optimization.
The Elusive Role of High Dimensions in Modern Optimization and Generalization
Delve into the fascinating role of high-dimensionality in deep learning optimization, exploring how modern neural networks challenge traditional theoretical constraints and achieve unprecedented success.
Model Stealing for Low-Rank Language Models Through Reverse Engineering
Discover groundbreaking mathematical techniques for reverse-engineering language models through conditional queries and barycentric spanners, exploring MIT's innovative approach to model extraction.