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

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Coffee and Check-In
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Opening Remarks
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Randomized Numerical Linear Algebra: Overview
Petros Drineas (Purdue University)
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Randomized Least Squares Regression: Combining Model- and Algorithm-Induced Uncertainties
Ilse Ipsen (North Carolina State University)
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Break
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Advanced Techniques for Low-Rank Matrix Approximation
Ming Gu (UC Berkeley)
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Recent Advances in Positive Semidefinite Matrix Approximation
Cameron Musco (Microsoft Research New England)
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Remembering Michael Cohen
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Lunch
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Reconstructing Continuous Signals via Leverage Score Sampling
Chris Musco (Princeton University)
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Asympirical Analysis: Theory Informs Practice
Ping Ma (University of Georgia)
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Break
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How Randomized Analysis Can Help Us Do “Data Science:” Examples in Deep Learning and Graph Analysis
Fred Roosta (University of Queensland)
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Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks
Michael Mahoney (International Computer Science Institute and UC Berkeley)
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Welcome Reception
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Coffee and Check-In
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Stochastic Quasi-Gradient Methods: Variance Reduction via Jacobian Sketching
Peter Richtarik (University of Edinburgh)
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Randomized Riemannian Preconditioning for Quadratically Constrained Problems
Haim Avron (Tel Aviv University)
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Break
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Convergence Theory for Iterative Eigensolvers
Mark Embree (Virginia Tech University)
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Randomized Algorithms for Computing Full Matrix Factorizations
Gunnar Martinsson (University of Texas at Austin)
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Lunch
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Applying Randomized Methods for Zonotope Enumeration and Monte Carlo Methods for Solving Linear Systems
David Gleich (Purdue University)
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Error Estimation for Randomized Numerical Linear Algebra: Bootstrap Methods
Miles Lopes (UC Davis)
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Break
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Scalable Algorithmic Primitives for Data Science
Richard Peng (Georgia Institute of Technology)
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Coffee and Check-In
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Hessian Matrix Inversion in 10^10 Dimensions with Parametric Bootstraps
Uros Seljak (UC Berkeley)
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Ridge Regression and Deterministic Ridge Leverage Score Sampling
Shannon McCurdy (UC Berkeley)
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Break
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Large Scale Stochastic Training of Neural Networks
Amir Gholaminejad (UC Berkeley)
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Randomized Constrained Matrix Decompositions
Benjamin Erichson (University of Washington)
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Lunch
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Adventures with Randomized Algebra for Extreme-Scale Signal Processing
Anshumali Shrivastava (Rice University)
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Unbiased Estimates for Linear Regression via Volume Sampling
Michal Derezinski (University of Michigan)
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Break
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Smoothed Analysis of Low Rank Solutions to Semidefinite Programs via Burer Monteiro Factorization
Praneeth Netrapalli (Microsoft Research India)
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Coffee and Check-In
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Matrix-free Construction of HSS Representations Using Adaptive Randomized Sampling
Xiaoye S. Li (LBNL)
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Fast SVD in the Presence of Noise
Matan Gavish (Hebrew University of Jerusalem)
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Break
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A Fast Algorithm for Computing the Maximum Weight Base in a Linear Matroid
Huy Nguyen (Northeastern University)
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Matrix Martingales in Randomized Numerical Linear Algebra
Rasmus Kyng (ETH Zurich)
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Lunch
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Fast Algorithms for Multivariate ODEs arising in Sampling and Learning
Santosh Vempala (Georgia Institute of Technology)
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Spectrally Robust Graph Isomorphism
Yiannis Koutis (New Jersey Institute of Technology)
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Break
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Subspace Clustering using Ensembles of K-Subspaces
Laura Balzano (University of Michigan)
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Closing Remarks