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20152019
most citedLow-rank Solutions of Linear Matrix Equations via Procrustes Flow

137 citations · 419 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.LG2019★ 29 cited

Learning Linear Dynamical Systems with Semi-Parametric Least Squares

Max Simchowitz, Ross Boczar, Benjamin Recht

We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in ca…

cs.LG2018★ 3 cited

A Successive-Elimination Approach to Adaptive Robotic Sensing

Esther Rolf, David Fridovich-Keil, Max Simchowitz +2

We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background sign…

cs.LG2018

Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law

Max Simchowitz, Ahmed El Alaoui, Benjamin Recht

We prove a \emph{query complexity} lower bound for approximating the top dimensional eigenspace of a matrix. We consider an oracle model where, given a symmetric matrix $\mathb…

cs.LG2018★ 46 cited

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification

Max Simchowitz, Horia Mania, Stephen Tu +2

We prove that the ordinary least-squares (OLS) estimator attains nearly minimax optimal performance for the identification of linear dynamical systems from a single observed trajec…

cs.LG2017★ 7 cited

On the Gap Between Strict-Saddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation

Max Simchowitz, Ahmed El Alaoui, Benjamin Recht

We prove a \emph{query complexity} lower bound on rank-one principal component analysis (PCA). We consider an oracle model where, given a symmetric matrix $M \in \mathbb{R}^{d \tim…

cs.LG2016★ 2 cited

Best-of-K Bandits

Max Simchowitz, Kevin Jamieson, Benjamin Recht

This paper studies the Best-of-K Bandit game: At each time the player chooses a subset S among all N-choose-K possible options and observes reward max(X(i) : i in S) where X is a r…