42 citations · 247 across the 29 of their papers we have counts for
4 papers · 1 filter
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition
Michał Dereziński, Elizaveta Rebrova
Sketch-and-project is a framework which unifies many known iterative methods for solving linear systems and their variants, as well as further extensions to non-linear optimization…
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
Michał Dereziński
Stochastic variance reduction has proven effective at accelerating first-order algorithms for solving convex finite-sum optimization tasks such as empirical risk minimization. Inco…
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
Sen Na, Michał Dereziński, Michael W. Mahoney
We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the algorithm is given an oracle access to a stochastic…
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
Michał Dereziński, Jonathan Lacotte, Mert Pilanci +1
In second-order optimization, a potential bottleneck can be computing the Hessian matrix of the optimized function at every iteration. Randomized sketching has emerged as a powerfu…