From the 1 of 9 linked papers with an AI index.
9 papers
Entropy Equivalence Testing
Clément L. Canonne, Yash Pote, Jonathan Scarlett +1
The paper defines entropy equivalence testing, a relaxation of distribution closeness testing that distinguishes identical distributions from those whose Shannon entropies differ b…
Distributed Gaussian Mean Testing under Communication Constraints: messages, samples, and coins
Clément L. Canonne, Nimitt
We revisit the problem of Gaussian mean testing in a distributed, communication constrained setting, where each of users independently observes samples from an unknown -dime…
NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria
Eason Yu, Tzu Hao Liu, Clément L. Canonne +4
Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent multi-round regularization meth…
Uniformity Testing under User-Level Local Privacy
Clément L. Canonne, Abigail Gentle, Vikrant Singhal
We initiate the study of distribution testing under \emph{user-level} local differential privacy, where each of users contributes samples from the unknown underlying distri…
Instance-Optimal Uniformity Testing and Tracking
Guy Blanc, Clément L. Canonne, Erik Waingarten
In the uniformity testing task, an algorithm is provided with samples from an unknown probability distribution over a (known) finite domain, and must decide whether it is the unifo…
Zero-Shot Attribution for Large Language Models: A Distribution Testing Approach
Clément L. Canonne, Yash Pote, Uddalok Sarkar
A growing fraction of all code is sampled from Large Language Models (LLMs). We investigate the problem of attributing code generated by language models using hypothesis testing to…