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02/25/2023
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
A fundamental question in reinforcement learning theory is: suppose the ...
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03/06/2022
Coresets for Data Discretization and Sine Wave Fitting
In the monitoring problem, the input is an unbounded stream P=p_1,p_2⋯ o...
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02/11/2022
Computational-Statistical Gaps in Reinforcement Learning
Reinforcement learning with function approximation has recently achieved...
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11/08/2021
Realizable Learning is All You Need
The equivalence of realizable and agnostic learnability is a fundamental...
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06/14/2021
Boosting in the Presence of Massart Noise
We study the problem of boosting the accuracy of a weak learner in the (...
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03/13/2021
Fooling Gaussian PTFs via Local Hyperconcentration
We give a pseudorandom generator that fools degree-d polynomial threshol...
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02/09/2021
Bounded Memory Active Learning through Enriched Queries
The explosive growth of easily-accessible unlabeled data has lead to gro...
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01/06/2021
Highway: Efficient Consensus with Flexible Finality
There has been recently a lot of progress in designing efficient partial...
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05/13/2020
Robustly Learning any Clusterable Mixture of Gaussians
We study the efficient learnability of high-dimensional Gaussian mixture...
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01/15/2020
Noise-tolerant, Reliable Active Classification with Comparison Queries
With the explosion of massive, widely available unlabeled data in the pa...
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11/19/2019
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
We study high-dimensional sparse estimation tasks in a robust setting wh...
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02/10/2019
The Optimal Approximation Factor in Density Estimation
Consider the following problem: given two arbitrary densities q_1,q_2 an...
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11/06/2018
The entropy of lies: playing twenty questions with a liar
`Twenty questions' is a guessing game played by two players: Bob thinks ...
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09/15/2017
The Orthogonal Vectors Conjecture for Branching Programs and Formulas
In the Orthogonal Vectors (OV) problem, we wish to determine if there is...
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06/23/2016
Robust Learning of Fixed-Structure Bayesian Networks
We investigate the problem of learning Bayesian networks in an agnostic ...
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04/21/2016
