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)
36:16Latent Variable models and Subset Smoothing (SIMONS-TCS-ML)
Ravi Kannan introduces the 'Subset Smoothed' polytope K' for learning latent variable models, addressing challenges where data points lie far outside the latent polytope K due to significant perturbations.
Ravi Kannan (Simons Institute, UC Berkeley)
The Role of TCS in Modern Machine Learning
A number of Latent Variable Models in Machine Learning (including Mixture Models, Topic Models, Stochastic block models and Mixed Membership Community Mod els) can be abstracted to the geometric problem of learn ing a latent polytope K given data points, each obtained by randomly perturbing a latent point in K. The challenge is that perturbations are typically much larger than the dimensions of K and so data points lie (far) outside K. To tackle this, we introduce the “Subset Smoothed” polytope K′ which is the convex hull of (n/k) points, each obtained by averaging a k− subset of the n data points. [k is a parameter.] We will observe that K′ ≈ K under reasonable assumptions on data. We will also observe that K′ has a polynomial time optimization oracle. These simple observations are the starting point of our provable algorithm for learning K which the talk will describe.
Red Points and Blue Points (SIMONS-TCS-ML)
Next · Lecture 19Enhanced and Efficient Reasoning in Large Language Models (SIMONS-TCS-ML)
Seminar notes & discussion
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