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Showing papers from Indiana University Show all papers

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NeurIPS 2026U California, RiversideIndianaTest-time compute

Test-time Scaling for Diffusion Language Models with Frequency-Aware Remasking

Bowen Zuo, Yue Yu, Dongruo Zhou, Yinglun Zhu

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

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45%Niche pick
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NeurIPS 2026U California, San DiegoIndianaThe Ohio StateUCSDAlignment & preference optimization

Learnable Chernoff Baselines for Provable Inference-Time Alignment

Sunil Madhow, Yuchen Liang, Ness Shroff, Yingbin Liang and 1 more

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

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NeurIPS 2026MLCommonsValo HealthFactoredIndianaSage BionetworksAI policy & governance

Decentralized AI Governance Must Decouple Policy Processing from Capability Enforcement

Hasan Kassem, Christoforos Anagnostopoulos, Alejandro Aristizabal, Spyridon Bakas and 26 more

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026IndianaDeep RL

A New Framework for Quantum Reinforcement Learning

Alireza Tavakoli, Mohsen Heidari

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026SpotlightIndianaAlignment & preference optimization

Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior

INNSteer learns invertible latent transformations to apply input-dependent nonlinear steering, improving LLM behavior control over linear baselines.

Tuc Nguyen, Thai Le

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

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NeurIPS 2026Georgia Institute of TechnologyIndianaTime series forecasting

CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series

CAST is a causal successor operator for distribution-valued time series that preserves the simplex via transport, avoiding transition-kernel aliasing to achieve top ranks across 11 benchmarks.

Jiecheng Lu, Jieqi Di, Runhua Wu, Yuwei Zhou

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

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 2/5