1 citations · 1 across the 4 of their papers we have counts for
4 papers
Distribution Testing in the Presence of Arbitrarily Dominant Noise with Verification Queries
Hadley Black, Christopher Ye
We study distribution testing without direct access to a source of relevant data, but rather to one where only a tiny fraction is relevant. To enable this, we introduce the followi…
From Generative to Episodic: Sample-Efficient Replicable Reinforcement Learning
Max Hopkins, Sihan Liu, Christopher Ye +1
The epidemic failure of replicability across empirical science and machine learning has recently motivated the formal study of replicable learning algorithms [Impagliazzo et al. (2…
Replicable Distribution Testing
Ilias Diakonikolas, Jingyi Gao, Daniel Kane +2
We initiate a systematic investigation of distribution testing in the framework of algorithmic replicability. Specifically, given independent samples from a collection of probabili…
Replicable Uniformity Testing
Sihan Liu, Christopher Ye
Uniformity testing is arguably one of the most fundamental distribution testing problems. Given sample access to an unknown distribution on , one must decide if $…