6 citations · 8 across the 11 of their papers we have counts for
9 papers
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…
Simpler Distribution Testing with Little Memory
Clément L. Canonne, Joy Qiping Yang
We consider the question of distribution testing (specifically, uniformity and closeness testing) in the streaming setting, \ie under stringent memory constraints. We improve on th…
Tight Bounds for Machine Unlearning via Differential Privacy
Yiyang Huang, Clément L. Canonne
We consider the formulation of "machine unlearning" of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called "right to be forgotten" by requiring that…
Private Distribution Learning with Public Data: The View from Sample Compression
Shai Ben-David, Alex Bie, Clément L. Canonne +2
We study the problem of private distribution learning with access to public data. In this setup, which we refer to as public-private learning, the learner is given public and priva…
Near-Optimal Degree Testing for Bayes Nets
Vipul Arora, Arnab Bhattacharyya, Clément L. Canonne +1
This paper considers the problem of testing the maximum in-degree of the Bayes net underlying an unknown probability distribution over , given sample access to .…
Concentration Bounds for Discrete Distribution Estimation in KL Divergence
Clément L. Canonne, Ziteng Sun, Ananda Theertha Suresh
We study the problem of discrete distribution estimation in KL divergence and provide concentration bounds for the Laplace estimator. We show that the deviation from mean scales as…