Playlist: 28 videos

Randomized Numerical Linear Algebra and Applications

0:31:58

Randomized Numerical Linear Algebra: Overview

Petros Drineas (Purdue University)
https://simons.berkeley.edu/talks/tbd-24
Randomized Numerical Linear Algebra and Applications
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0:24:26

Randomized Least Squares Regression: Combining Model- and Algorithm-Induced Uncertainties

Ilse Ipsen (North Carolina State University)
https://simons.berkeley.edu/talks/randomized-least-squares-regression-combining-model-and-algorithm-induced-uncertainties
Randomized Numerical Linear Algebra and Applications
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0:32:1

Advanced Techniques for Low-Rank Matrix Approximation

Ming Gu (UC Berkeley)
https://simons.berkeley.edu/talks/advanced-techniques-low-rank-matrix-approximation
Randomized Numerical Linear Algebra and Applications
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0:29:8

Recent Advances in Positive Semidefinite Matrix Approximation

Cameron Musco (Microsoft Research New England)
https://simons.berkeley.edu/talks/recent-advances-positive-semidefinite-matrix-approximation
Randomized Numerical Linear Algebra and Applications
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0:32:16

Reconstructing Continuous Signals via Leverage Score Sampling

Chris Musco (Princeton University)
https://simons.berkeley.edu/talks/reconstructing-continuous-signals-leverage-score-sampling
Randomized Numerical Linear Algebra and Applications
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0:26:8

Asympirical Analysis: Theory Informs Practice

Ping Ma (University of Georgia)
https://simons.berkeley.edu/talks/asympirical-analysis-theory-informs-practice
Randomized Numerical Linear Algebra and Applications
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0:32:19

How Randomized Analysis Can Help Us Do “Data Science:” Examples in Deep Learning and Graph Analysis

Fred Roosta (University of Queensland)
https://simons.berkeley.edu/talks/how-randomized-analysis-can-help-us-do-data-science-examples-deep-learning-and-graph-analysis
Randomized Numerical Linear Algebra and Applications
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0:38:13

Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks

Michael Mahoney (International Computer Science Institute and UC Berkeley)
https://simons.berkeley.edu/talks/9-24-mahoney-deep-learning
Randomized Numerical Linear Algebra and Applications
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0:32:36

Stochastic Quasi-Gradient Methods: Variance Reduction via Jacobian Sketching

Peter Richtarik (University of Edinburgh)
https://simons.berkeley.edu/talks/stochastic-quasi-gradient-methods-variance-reduction-jacobian-sketching
Randomized Numerical Linear Algebra and Applications
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0:30:37

Randomized Riemannian Preconditioning for Quadratically Constrained Problems

Haim Avron (Tel Aviv University)
https://simons.berkeley.edu/talks/randomized-riemannian-preconditionning-quadratically-constrained-problems
Randomized Numerical Linear Algebra and Applications
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