• Click on the titles of individual talks for abstract, slides and archived video.

–
Coffee and Check-In
–
Opening Remarks
–
Randomized Numerical Linear Algebra: Overview

Petros Drineas (Purdue University)
Video
–
Randomized Least Squares Regression: Combining Model- and Algorithm-Induced Uncertainties

Ilse Ipsen (North Carolina State University)
Video
–
Break
–
Advanced Techniques for Low-Rank Matrix Approximation

Ming Gu (UC Berkeley)
Video
–
Recent Advances in Positive Semidefinite Matrix Approximation

Cameron Musco (Microsoft Research New England)
Video
–
Remembering Michael Cohen
–
Lunch
–
Reconstructing Continuous Signals via Leverage Score Sampling

Chris Musco (Princeton University)
Video
–
Asympirical Analysis: Theory Informs Practice

Ping Ma (University of Georgia)
Video
–
Break
–
How Randomized Analysis Can Help Us Do “Data Science:” Examples in Deep Learning and Graph Analysis

Fred Roosta (University of Queensland)
Video
–
Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks

Michael Mahoney (International Computer Science Institute and UC Berkeley)
Video
–
Welcome Reception
–
Coffee and Check-In
–
Stochastic Quasi-Gradient Methods: Variance Reduction via Jacobian Sketching

Peter Richtarik (University of Edinburgh)
Video
–
Randomized Riemannian Preconditioning for Quadratically Constrained Problems

Haim Avron (Tel Aviv University)
Video
–
Break
–
Convergence Theory for Iterative Eigensolvers

Mark Embree (Virginia Tech University)
Video
–
Randomized Algorithms for Computing Full Matrix Factorizations

Gunnar Martinsson (University of Texas at Austin)
Video
–
Lunch
–
Applying Randomized Methods for Zonotope Enumeration and Monte Carlo Methods for Solving Linear Systems

David Gleich (Purdue University)
Video
–
Error Estimation for Randomized Numerical Linear Algebra: Bootstrap Methods

Miles Lopes (UC Davis)
Video
–
Break
–
Scalable Algorithmic Primitives for Data Science

Richard Peng (Georgia Institute of Technology)
Video
–
Coffee and Check-In
–
Hessian Matrix Inversion in 10^10 Dimensions with Parametric Bootstraps

Uros Seljak (UC Berkeley)
Video
–
Ridge Regression and Deterministic Ridge Leverage Score Sampling

Shannon McCurdy (Ancestry.com)
Video
–
Break
–
Large Scale Stochastic Training of Neural Networks

Amir Gholaminejad Gholaminejad (UC Berkeley)
Video
–
Randomized Constrained Matrix Decompositions

Benjamin Erichson (University of Washington)
Video
–
Lunch
–
Adventures with Randomized Algebra for Extreme-Scale Signal Processing

Anshumali Shrivastava (Rice University)
Video
–
Unbiased Estimates for Linear Regression via Volume Sampling

Michal Derezinski (University of Michigan)
Video
–
Break
–
Smoothed Analysis of Low Rank Solutions to Semidefinite Programs via Burer Monteiro Factorization

Praneeth Netrapalli (Microsoft Research India)
Video
–
Coffee and Check-In
–
Matrix-free Construction of HSS Representations Using Adaptive Randomized Sampling

Xiaoye S. Li (LBNL)
Video
–
Fast SVD in the Presence of Noise

Matan Gavish (Hebrew University of Jerusalem)
–
Break
–
A Fast Algorithm for Computing the Maximum Weight Base in a Linear Matroid

Huy Nguyen (Northeastern University)
Video
–
Matrix Martingales in Randomized Numerical Linear Algebra

Rasmus Kyng (ETH Zurich)
Video
–
Lunch
–
Fast Algorithms for Multivariate ODEs arising in Sampling and Learning

Santosh Vempala (Georgia Institute of Technology)
Video
–
Spectrally Robust Graph Isomorphism

Yiannis Koutis (New Jersey Institute of Technology)
Video
–
Break
–
Subspace Clustering using Ensembles of K-Subspaces

Laura Balzano (University of Michigan)
Video
–
Closing Remarks