51 citations · 68 across the 22 of their papers we have counts for
24 papers
When Metropolis and Hastings Meet Bradley and Terry: Exact MCMC From Preference Voting
Ariel Smogorghevski, Nir Rosenfeld, Yaniv Romano
Sampling from distributions conditioned on desired semantic properties is an emerging challenge in modern generative modeling. Metropolis-Hastings (MH) provides a principled route…
Evolutionary Prediction Games
Eden Saig, Nir Rosenfeld
When a prediction algorithm serves a collection of users, disparities in prediction quality are likely to emerge. If users respond to accurate predictions by increasing engagement,…
Classification Under Strategic Self-Selection
Guy Horowitz, Yonatan Sommer, Moran Koren +1
When users stand to gain from certain predictions, they are prone to act strategically to obtain favorable predictive outcomes. Whereas most works on strategic classification consi…
One-Shot Strategic Classification Under Unknown Costs
Elan Rosenfeld, Nir Rosenfeld
The goal of strategic classification is to learn decision rules which are robust to strategic input manipulation. Earlier works assume that these responses are known; while some re…
Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias
Itay Itzhak, Gabriel Stanovsky, Nir Rosenfeld +1
Recent studies show that instruction tuning (IT) and reinforcement learning from human feedback (RLHF) improve the abilities of large language models (LMs) dramatically. While thes…
Delegated Classification
Eden Saig, Inbal Talgam-Cohen, Nir Rosenfeld
When machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance. In this work, we propose a theoretical framew…