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Daniel Halpern

14 accepted papers

2026

Robust AI Evaluation through Maximal Lotteries

ICML 2026poster

The standard way to evaluate language models on subjective tasks is through pairwise comparisons: an annotator chooses the "better" of two model responses for a given prompt. These comparisons are then aggregated into a single ranking via the Bradley–Terry (BT) framework, forcing heterogeneous prefe…

Cited by 0SourceScholar
2024

Axioms for AI Alignment from Human Feedback

NeurIPS 2024spotlight

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 pairwise comparisons made by humans. The problem of learning a reward function is one of preference aggregation that, we…

Cited by 17SourcePDFScholar
2023

Representation with Incomplete Votes

AAAI 2023technical

Platforms for online civic participation rely heavily on methods for condensing thousands of comments into a relevant handful, based on whether participants agree or disagree with them. These methods should guarantee fair representation of the participants, as their outcomes may affect the health of…

2022

How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary Issues

AAAI 2022technical

Aggregating opinions of a collection of agents is a question of interest to a broad array of researchers, ranging from ensemble-learning theorists to political scientists designing democratic institutions. This work investigates the optimal number of agents needed to decide on a binary issue under m…

Cited by 1SourcePDFScholar
2021

Aggregating Binary Judgments Ranked by Accuracy

AAAI 2021technical

We revisit the fundamental problem of predicting a binary ground truth based on independent binary judgments provided by experts. When the accuracy levels of the experts are known, the problem can be solved easily through maximum likelihood estimation. We consider, however, a setting in which we are…