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Paul Valiant

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Depth Separations in Neural Networks: Separating the Dimension from the Accuracy

Feb 11, 2024
Itay Safran, Daniel Reichman, Paul Valiant

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Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+α$ Moments

Nov 21, 2023
Trung Dang, Jasper C. H. Lee, Maoyuan Song, Paul Valiant

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How Many Neurons Does it Take to Approximate the Maximum?

Jul 18, 2023
Itay Safran, Daniel Reichman, Paul Valiant

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Finite-Sample Maximum Likelihood Estimation of Location

Jun 06, 2022
Shivam Gupta, Jasper C. H. Lee, Eric Price, Paul Valiant

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Optimal Sub-Gaussian Mean Estimation in $\mathbb{R}$

Nov 17, 2020
Jasper C. H. Lee, Paul Valiant

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How bad is worst-case data if you know where it comes from?

Nov 09, 2019
Justin Y. Chen, Gregory Valiant, Paul Valiant

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Uncertainty about Uncertainty: Near-Optimal Adaptive Algorithms for Estimating Binary Mixtures of Unknown Coins

Apr 19, 2019
Jasper C. H. Lee, Paul Valiant

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Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process

Apr 19, 2019
Guy Blanc, Neha Gupta, Gregory Valiant, Paul Valiant

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Instance Optimal Learning

Nov 11, 2015
Gregory Valiant, Paul Valiant

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Optimal Algorithms for Testing Closeness of Discrete Distributions

Aug 19, 2013
Siu-On Chan, Ilias Diakonikolas, Gregory Valiant, Paul Valiant

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