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09/18/2023
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles
The key assumption underlying linear Markov Decision Processes (MDPs) is...
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06/03/2023
Provable benefits of score matching
Score matching is an alternative to maximum likelihood (ML) for estimati...
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05/26/2023
Feature Adaptation for Sparse Linear Regression
Sparse linear regression is a central problem in high-dimensional statis...
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06/07/2022
Learning in Observable POMDPs, without Computationally Intractable Oracles
Much of reinforcement learning theory is built on top of oracles that ar...
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05/28/2022
Provably Auditing Ordinary Least Squares in Low Dimensions
Measuring the stability of conclusions derived from Ordinary Least Squar...
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03/05/2022
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs
Sparse linear regression with ill-conditioned Gaussian random designs is...
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01/12/2022
Planning in Observable POMDPs in Quasipolynomial Time
Partially Observable Markov Decision Processes (POMDPs) are a natural an...
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10/06/2021
Robust Generalized Method of Moments: A Finite Sample Viewpoint
For many inference problems in statistics and econometrics, the unknown ...
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06/17/2021
On the Power of Preconditioning in Sparse Linear Regression
Sparse linear regression is a fundamental problem in high-dimensional st...
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07/29/2020
Truncated Linear Regression in High Dimensions
As in standard linear regression, in truncated linear regression, we are...
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06/07/2020
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Generative neural networks have been empirically found very promising in...
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10/27/2019
Near-Optimal Bounds for Online Caching with Machine Learned Advice
In the model of online caching with machine learned advice, introduced b...
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09/24/2019
Conditional Hardness of Earth Mover Distance
The Earth Mover Distance (EMD) between two sets of points A, B ⊆R^d with...
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07/12/2018
