Theoretical Foundations of Modern Machine Learning Paradigms
38:15Foundations, Privacy & Generalization Challenges in Modern Machine Learning
36:41Efficient Algorithms for Reliable Machine Learning
37:26Computational Thinking on Learning Models
38:30On the Komlós Conjecture
33:41Multi-objective Learning: An Algorithmic Toolbox for Optimal Predictions Anytime Anywhere
37:40Approximately Packing Dijoins Via Nowhere-Zero Flows
33:46Lightning Talks
56:01Venturing out into the world with AI
44:41Unfamiliar Terrain
29:51Astral Space: Convex Analysis at Infinity
44:01AI and Safety
29:26The Role of TCS in Understanding (AI) Consciousness
43:21The Conscious Turing Machine (CTM), a formally defined Theoretical model of Consciousness
45:00Theory of Modern AI: Learning Theoretic, Game Theoretic, and Algorithmic Perspectives
40:41An Occam's Razor Principle for Transformers?
39:55Red Points and Blue Points
38:15Latent Variable models and Subset Smoothing
29:26Enhanced and Efficient Reasoning in Large Language Models
36:16Foundations, Privacy & Generalization Challenges in Modern Machine Learning
Katrina Ligett discusses the pivotal contributions and ongoing challenges for theoretical computer science in advancing the state of modern machine learning.
Katrina Ligett (Hebrew University of Jerusalem) The Role of TCS in Modern Machine Learning
Theoretical Foundations of Modern Machine Learning Paradigms
Next · Lecture 3Efficient Algorithms for Reliable Machine Learning