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8 videos found
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
5views
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
10views
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
8views
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
6views
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
11views
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
14views
Foundations,
Machine Learning,
Optimization for ML & AI Seminar Series
Training Neural Networks at Any Scale
01:00:09
Training Neural Networks at Any Scale
10views
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
17views
Foundations,
Machine Learning,
Optimization for ML & AI Seminar Series,
TILOS Seminar Series
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This project is supported by the U.S. National Science Foundation under CCF-2112665. 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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