activity
20222024
most citedNear-Optimal Bounds for Testing Histogram Distributions

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DS2024

Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs

Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2

We study the efficient learnability of low-degree polynomial threshold functions (PTFs) in the presence of a constant fraction of adversarial corruptions. Our main algorithmic resu…

cs.LG2023

Online Robust Mean Estimation

Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao +1

We study the problem of high-dimensional robust mean estimation in an online setting. Specifically, we consider a scenario where sensors are measuring some common, ongoing phen…

cs.LG2023

Efficient Testable Learning of Halfspaces with Adversarial Label Noise

Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2

We give the first polynomial-time algorithm for the testable learning of halfspaces in the presence of adversarial label noise under the Gaussian distribution. In the recently intr…

cs.LG20231 cited

Exponential Hardness of Reinforcement Learning with Linear Function Approximation

Daniel Kane, Sihan Liu, Shachar Lovett +3

A fundamental question in reinforcement learning theory is: suppose the optimal value functions are linear in given features, can we learn them efficiently? This problem's counterp…

cs.DS20221 cited

Near-Optimal Bounds for Testing Histogram Distributions

Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane +1

We investigate the problem of testing whether a discrete probability distribution over an ordered domain is a histogram on a specified number of bins. One of the most common tools…