Aloni Cohen

2 accepted papers

2026

Barriers to Counterfactual Credit Attribution for Autoregressive Models

ICML 2026poster

Generative AI disrupts the practice of giving credit to work that came before. Ideally, a generative model would give credit to any work on which its output depends in a significant way. *Counterfactual credit attribution (CCA)* is a technical condition formalizing this goal---a relaxation of differ…

Cited by 0 Source ↗Scholar ↗

2025

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models

Aloni Cohen

NeurIPS 2025spotlight

Are there any conditions under which a generative model’s outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak [ICML 2023]. They define _near access-freeness (NAF)_ and propose it as…

Cited by 0 Source ↗Scholar ↗