3 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
Incentives in Federated Learning with Heterogeneous Agents
Ariel D. Procaccia, Han Shao, Itai Shapira
Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the cont…
Learning Social Welfare Functions
Kanad Shrikar Pardeshi, Itai Shapira, Ariel D. Procaccia +1
Is it possible to understand or imitate a policy maker's rationale by looking at past decisions they made? We formalize this question as the problem of learning social welfare func…
Bias Detection Via Signaling
Yiling Chen, Tao Lin, Ariel D. Procaccia +2
We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased t…
Axioms for AI Alignment from Human Feedback
Luise Ge, Daniel Halpern, Evi Micha +4
In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on…
Generative Social Choice
Sara Fish, Paul Gölz, David C. Parkes +4
The mathematical study of voting, social choice theory, has traditionally only been applicable to choices among a few predetermined alternatives, but not to open-ended decisions su…