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Simons Institute: Theoretical Foundations of Modern Machine Learning →0/19
1

Talk by Pranjal Awasthi (Google) (SIMONS-TCS-ML)

38:15
2

Talk by Katrina Ligett (Hebrew University of Jerusalem) (SIMONS-TCS-ML)

36:41
3

Efficient Algorithms for Reliable Machine Learning (SIMONS-TCS-ML)

37:26
4

Computational Thinking on Learning Models (SIMONS-TCS-ML)

38:30
5

On the Komlós Conjecture (SIMONS-TCS-ML)

33:41
6

Multi-objective Learning: An Algorithmic Toolbox for Optimal Predictions Anytime Anywhere! (SIMONS-TCS-ML)

37:40
7

Approximately Packing Dijoins Via Nowhere-Zero Flows (SIMONS-TCS-ML)

33:46
8

Lightning Talks! (SIMONS-TCS-ML)

56:01
9

Venturing out into the world with AI (SIMONS-TCS-ML)

44:41
10

Unfamiliar Terrain (SIMONS-TCS-ML)

29:51
11

Astral Space: Convex Analysis at Infinity (SIMONS-TCS-ML)

44:01
12

AI and Safety (SIMONS-TCS-ML)

29:26
13

The Role of TCS in Understanding (AI) Consciousness (SIMONS-TCS-ML)

43:21
14

The Conscious Turing Machine (CTM), a formally defined Theoretical model of Consciousness (SIMONS-TCS-ML)

45:00
15

Theory of Modern AI: Learning Theoretic, Game Theoretic, and Algorithmic Perspectives (SIMONS-TCS-ML)

40:41
16

An Occam's Razor Principle for Transformers? (SIMONS-TCS-ML)

39:55
17

Red Points and Blue Points (SIMONS-TCS-ML)

38:15
18

Latent Variable models and Subset Smoothing (SIMONS-TCS-ML)

29:26
19

Enhanced and Efficient Reasoning in Large Language Models (SIMONS-TCS-ML)

36:16

Efficient Algorithms for Reliable Machine Learning (SIMONS-TCS-ML)

Adam KlivansSimons Institute (UC Berkeley)

Professor

37:26·2026·research level

Adam Klivans presents new learning models where algorithms either certify accuracy or abstain, moving beyond traditional distributional assumptions toward provably correct, reliable machine learning.

Theory & AlgosAlgorithms & Data StructuresReliable MLRobust LearningWorst-case Analysis

Adam Klivans (University of Texas, Austin)

The Role of TCS in Modern Machine Learning

Algorithms for supervised learning typically require some sort of distributional assumption (e.g., Gaussianity), in contrast to the traditional worst-case analysis paradigm from theoretical computer science. This leads to algorithms that succeed only under hard-to-verify assumptions, undermining the very notion of provable correctness.

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Talk by Katrina Ligett (Hebrew University of Jerusalem) (SIMONS-TCS-ML)

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Computational Thinking on Learning Models (SIMONS-TCS-ML)

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