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Showing papers from Harvard University/Google Research Show all papers

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NeurIPS 2026HarvardHarvard University/Google ResearLearning theory

Learning to Persuade a Biased Receiver

Proposes safe exploration to learn a receiver's unknown belief bias via signaling, achieving optimal O(log log T) regret by exploiting asymmetric probing costs.

Yuqi Pan, Sadie Zhao, Milind Tambe, Yiling Chen

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 2/5
medium 3/10
strict 2/5
74%Highly rated
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NeurIPS 2026HarvardTencentShandongHarvard University/Google ResearAlignment & preference optimization

LLM Active Alignment: A Nash Equilibrium Perspective

A game-theoretic framework predicts and steers LLM populations via Nash equilibrium analysis, deriving closed-form alignments that prevent political exclusion and guide socially desirable outcomes.

Tonghan Wang, Yuqi Pan, Xinyi Yang, Xinrui Song and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5
91%Must read
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NeurIPS 2026HarvardHarvard University/Google ResearHuman-AI interaction

Embeddings for Preferences, Not Semantics

Text embeddings should encode preferential rather than semantic similarity for collective decisions; breaking nuisance correlation with synthetic training improves preference prediction across 11 deliberation datasets.

Carter Blair, Ariel Procaccia, Milind Tambe

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 2/5
80%Must read
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NeurIPS 2026HarvardGeorgia Institute of TechnologyHarvard University/Google ResearOffline RL

Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning

BOOST uses bilevel optimization to learn trajectory weights from held-out validation, improving multi-turn LLM fine-tuning by upweighting high-quality synthetic data aligned with real distributions.

Shresth Verma, Mauricio Tec, Cheol Woo Kim, Kai Wang and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5