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20152022
most citedReducibility and Computational Lower Bounds for Problems with Planted Sparse Structure

31 citations · 123 across the 9 of their papers we have counts for

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Showing cs.CCShow all

6 papers · 1 filter

cs.CC2020★ 25 cited

Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent

Matthew Brennan, Guy Bresler, Samuel B. Hopkins +2

Researchers currently use a number of approaches to predict and substantiate information-computation gaps in high-dimensional statistical estimation problems. A prominent approach…

cs.CC2020★ 21 cited

Reducibility and Statistical-Computational Gaps from Secret Leakage

Matthew Brennan, Guy Bresler

Inference problems with conjectured statistical-computational gaps are ubiquitous throughout modern statistics, computer science and statistical physics. While there has been succe…

cs.CC2019★ 8 cited

Average-Case Lower Bounds for Learning Sparse Mixtures, Robust Estimation and Semirandom Adversaries

Matthew Brennan, Guy Bresler

This paper develops several average-case reduction techniques to show new hardness results for three central high-dimensional statistics problems, implying a statistical-computatio…

cs.CC2019

The Average-Case Complexity of Counting Cliques in Erdos-Renyi Hypergraphs

Enric Boix-Adserà, Matthew Brennan, Guy Bresler

We consider the problem of counting -cliques in -uniform Erdos-Renyi hypergraphs with edge density , and show that its fine-grained average-case complexity can…

cs.CC2019★ 20 cited

Optimal Average-Case Reductions to Sparse PCA: From Weak Assumptions to Strong Hardness

Matthew Brennan, Guy Bresler

In the past decade, sparse principal component analysis has emerged as an archetypal problem for illustrating statistical-computational tradeoffs. This trend has largely been drive…

cs.CC2018★ 31 cited

Reducibility and Computational Lower Bounds for Problems with Planted Sparse Structure

Matthew Brennan, Guy Bresler, Wasim Huleihel

The prototypical high-dimensional statistics problem entails finding a structured signal in noise. Many of these problems exhibit an intriguing phenomenon: the amount of data neede…