42 citations · 247 across the 29 of their papers we have counts for
7 papers · 1 filter
Exact expressions for double descent and implicit regularization via surrogate random design
Michał Dereziński, Feynman Liang, Michael W. Mahoney
Double descent refers to the phase transition that is exhibited by the generalization error of unregularized learning models when varying the ratio between the number of parameters…
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Mojmír Mutný, Michał Dereziński, Andreas Krause
We analyze the convergence rate of the randomized Newton-like method introduced by Qu et. al. (2016) for smooth and convex objectives, which uses random coordinate blocks of a Hess…
Unbiased estimators for random design regression
Michał Dereziński, Manfred K. Warmuth, Daniel Hsu
In linear regression we wish to estimate the optimum linear least squares predictor for a distribution over -dimensional input points and real-valued responses, based on a small…
Bayesian experimental design using regularized determinantal point processes
Michał Dereziński, Feynman Liang, Michael W. Mahoney
In experimental design, we are given vectors in dimensions, and our goal is to select of them to perform expensive measurements, e.g., to obtain labels/responses,…
Distributed estimation of the inverse Hessian by determinantal averaging
Michał Dereziński, Michael W. Mahoney
In distributed optimization and distributed numerical linear algebra, we often encounter an inversion bias: if we want to compute a quantity that depends on the inverse of a sum of…
Exact sampling of determinantal point processes with sublinear time preprocessing
Michał Dereziński, Daniele Calandriello, Michal Valko
We study the complexity of sampling from a distribution over all index subsets of the set with the probability of a subset proportional to the determinant of the…
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Michał Dereziński, Kenneth L. Clarkson, Michael W. Mahoney +1
In experimental design, we are given a large collection of vectors, each with a hidden response value that we assume derives from an underlying linear model, and we wish to pick a…