Talk by Pranjal Awasthi (Google) (SIMONS-TCS-ML)
38:15Talk by Katrina Ligett (Hebrew University of Jerusalem) (SIMONS-TCS-ML)
36:41Efficient Algorithms for Reliable Machine Learning (SIMONS-TCS-ML)
37:26Computational Thinking on Learning Models (SIMONS-TCS-ML)
38:30On the Komlós Conjecture (SIMONS-TCS-ML)
33:41Multi-objective Learning: An Algorithmic Toolbox for Optimal Predictions Anytime Anywhere! (SIMONS-TCS-ML)
37:40Approximately Packing Dijoins Via Nowhere-Zero Flows (SIMONS-TCS-ML)
33:46Lightning Talks! (SIMONS-TCS-ML)
56:01Venturing out into the world with AI (SIMONS-TCS-ML)
44:41Unfamiliar Terrain (SIMONS-TCS-ML)
29:51Astral Space: Convex Analysis at Infinity (SIMONS-TCS-ML)
44:01AI and Safety (SIMONS-TCS-ML)
29:26The Role of TCS in Understanding (AI) Consciousness (SIMONS-TCS-ML)
43:21The Conscious Turing Machine (CTM), a formally defined Theoretical model of Consciousness (SIMONS-TCS-ML)
45:00Theory of Modern AI: Learning Theoretic, Game Theoretic, and Algorithmic Perspectives (SIMONS-TCS-ML)
40:41An Occam's Razor Principle for Transformers? (SIMONS-TCS-ML)
39:55Red Points and Blue Points (SIMONS-TCS-ML)
38:15Latent Variable models and Subset Smoothing (SIMONS-TCS-ML)
29:26Enhanced and Efficient Reasoning in Large Language Models (SIMONS-TCS-ML)
36:16Multi-objective Learning: An Algorithmic Toolbox for Optimal Predictions Anytime Anywhere! (SIMONS-TCS-ML)
Nika Haghtalab introduces multi-objective learning as a unifying framework, providing an algorithmic toolbox for learning models with guarantees across diverse tasks, enhancing fairness and robustness in ML.
Nika Haghtalab (UC Berkeley)
The Role of TCS in Modern Machine Learning
In this talk, I will introduce multiobjective learning as a unifying paradigm for learning models with performance guarantees across arbitrary downstream tasks and losses. I will present an algorithmic toolbox for learning such multiobjective models from a small number of samples and with modest computation. I will also highlight how this toolbox provides a useful lens for designing algorithms and obtaining improved or optimal guarantees for several general frameworks in ML theory, including multi-distribution learning, group distributionally robust learning, fairness in ML, calibration, and omniprediction.
On the Komlós Conjecture (SIMONS-TCS-ML)
Next · Lecture 7Approximately Packing Dijoins Via Nowhere-Zero Flows (SIMONS-TCS-ML)
Seminar notes & discussion
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