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10 videos found
Self-play Algorithms for Math Theorem Proving
00:53:25
Self-play Algorithms for Math Theorem Proving
300views
Machine Learning,
Optimization for ML & AI Seminar Series
A non-equilibrium phase transition with broken ergodicity causes double descent & benign overfitting
00:58:30
A non-equilibrium phase transition with broken ergodicity causes double descent & benign overfitting
348views
Machine Learning,
Optimization for ML & AI Seminar Series
A survey of the mixing times of the Proximal Sampler algorithm
00:56:52
A survey of the mixing times of the Proximal Sampler algorithm
322views
Foundations,
Optimization for ML & AI Seminar Series
Transformers Learn Generalizable Chain-of-Thought Reasoning via Gradient Descent
00:56:00
Transformers Learn Generalizable Chain-of-Thought Reasoning via Gradient Descent
374views
Foundations,
Machine Learning,
Optimization for ML & AI Seminar Series
(De)regularized Wasserstein Gradient Flows via Reproducing Kernels
01:00:42
(De)regularized Wasserstein Gradient Flows via Reproducing Kernels
327views
Foundations,
Machine Learning,
Optimization for ML & AI Seminar Series
Extended Convex Lifting for Policy Optimization in Control
00:55:51
Extended Convex Lifting for Policy Optimization in Control
174views
Optimization for ML & AI Seminar Series
Title slide for a presentation by Dr. Joel Tropp titled "Subspace Injections"
00:58:24
Randomized Linear Algebra with Subspace Injections
288views
Foundations,
Optimization for ML & AI Seminar Series
Stochastic-Gradient and Diagonal-Scaling Algorithms for Constrained Optimization and Learning
00:55:54
Stochastic-Gradient and Diagonal-Scaling Algorithms for Constrained Optimization and Learning
331views
Foundations,
Machine Learning,
Optimization for ML & AI Seminar Series
Training Neural Networks at Any Scale
01:00:09
Training Neural Networks at Any Scale
259views
Machine Learning,
Optimization for ML & AI Seminar Series
Title slide for a presentation by Dr. Courtney Paquette titled "High-dimensional Optimization in Machine Learning with Applications to Compute-Optimal Neural Scaling Laws"
00:54:16
High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws
331views
Foundations,
Machine Learning,
Optimization for ML & AI Seminar Series,
TILOS Seminar Series
NSF UC San Diego Halıcıoğlu School of Data Science and Computing Brown University MIT National University Penn Texas Yale

This project is supported by the U.S. National Science Foundation under awards 2112665 and 2622219. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. National Science Foundation.

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