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Showing papers from Boston University, Boston University Show all papers

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NeurIPS 2026Boston University, BostonCelonisUniversität KölnNational Institute of InformaticOptimization

Stable Max Coverage Under a Cardinality Constraint

Themistoklis Haris, Fabian Spaeh, Nithin Varma, Yuichi Yoshida

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

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NeurIPS 2026ColumbiaBoston University, BostonMIT, BostonIBM T.J. Watson Research CenterInformation retrieval

Robust Approximate Nearest Neighbor Search for Any Dataset

Alexandr Andoni, Themistoklis Haris, Esty Kelman, Krzysztof Onak

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Boston University, BostonHong Kong University of Science LLM evaluation & benchmarks

Online Evaluation of LLMs via Dyadic Designs

Jinglong Zhao, Zijie Zhou

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

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NeurIPS 2026BostonBoston University, BostonCornellOhio State University, ColumbusOhio State UniversityDatasets & benchmarks

Autonomous Driving Research Requires a Community-Driven Data Paradigm

Jinsu Yoo, Zanming Huang, Katie Luo, Zheda Mai and 4 more

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

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NeurIPS 2026BostonBoston University, BostonEfficient & distributed training

A Transformer-Derived Iterative Preconditioner

Patrick Lutz, Themistoklis Haris, Aditya Gangrade, Venkatesh Saligrama

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026MITMIT Lincoln Laboratory, MassachuBoston University, BostonMassachusetts Institute of TechnOffline RL

LASER: Latent Space Adjoint Matching for Support Constrained Entropy Regularized Offline RL

Songyuan Zhang, Oswin So, Eric Yu, Matthew Cleaveland and 2 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026BostonBoston University, BostonCreatify AIImage editing

GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing

GenScale benchmarks relative object scale in image generation and editing, finding current models unreliable, while Rescale improves scale plausibility via localized correction.

Lingxiao Li, Max Whitton, Ledell Wu, Boqing Gong

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
86%Must read
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NeurIPS 2026Northeastern University; STRU California, IrvineSystem Technology and ResearchBoston University, BostonSTRMulti-agent RL

Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning

MARS replaces ratio-based trust regions with a multiplicatively symmetric geometric barrier to cut variance and prevent probability collapse in multi-agent policy optimization. Across 47 tasks it matches or exceeds MAPPO and MASPO, with gains from barrier geometry rather than flexible boundaries.

Chulabhaya Wijesundara, Andrea Baisero, Zhongheng Li, Gregory D Castanon and 2 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 3/5
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NeurIPS 2026BostonBoston University, BostonMechanistic interpretability

Finding Interpretable Prompt-Specific Circuits in Language Models

ACC++ improves circuit tracing to extract interpretable prompt-specific language model circuits from single passes, revealing clustered indirect-object mechanisms and language-specific reused components.

Gabriel Franco, Lucas M Tassis, Azalea Rohr, Mark Crovella

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 1/5