2 citations · 3 across the 11 of their papers we have counts for
7 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…
The Proportional Veto Principle for Approval Ballots
Daniel Halpern, Ariel D. Procaccia, Warut Suksompong
The proportional veto principle, which captures the idea that a candidate vetoed by a large group of voters should not be chosen, has been studied for ranked ballots in single-winn…
Strategic Classification With Externalities
Safwan Hossain, Evi Micha, Yiling Chen +1
We propose a new variant of the strategic classification problem: a principal reveals a classifier, and agents report their (possibly manipulated) features to be classified. Mo…
Federated Assemblies
Daniel Halpern, Ariel D. Procaccia, Ehud Shapiro +1
A citizens' assembly is a group of people who are randomly selected to represent a larger population in a deliberation. While this approach has successfully strengthened democracy,…
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…