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NeurIPS 2026SLAC National Accelerator LaboraUniversity College London, UniveTechnische Universität MünchenUCLHumboldt Universität BerlinPhysics-informed ML & PDEs

Neural Scaling Laws in Particle Jets

Matthias Vigl, Nikita Pond, Nicole Hartman, Jackson Barr and 10 more

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

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medium 0/10
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45%Niche pick
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NeurIPS 2026Imperial College LondonUCLShanghai University of Finance aBayesian & probabilistic methods

Random-Projection Tree Stein Variational Gradient Descent

Shishuo Guo, Xiaoyuan Cheng, Zhuo Sun

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

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NeurIPS 2026Queen Mary University of LondonHuawei Technologies Ltd.UCLU BedfordshireQueen Mary University of London LLM evaluation & benchmarks

CADMA: Capacity-Aware Recall Decomposition for Generative Model Assessment

Debin Meng, Zheng Gao, Ziquan Liu, Yanran Li and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U OxfordUCLUniversity College LondonUniversity College LondonLLM evaluation & benchmarks

LLM-WikiRace: A Benchmark for Planning and Reasoning over Real-World Knowledge Graphs

Juliusz Ziomek, William Bankes, Lorenz Wolf, Shyam Sundhar Ramesh and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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80%Must read
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NeurIPS 2026UCLShanghai Jiao TongNanyang TechnologicalHarvardUniversity College London, UniveOffline RL

Fisher Decorator: Refining Flow Policy via A Local Transport Map

Fisher Decorator refines flow policies via local transport maps and Fisher-metric anisotropic optimization to fix isotropic approximation errors in offline RL.

Xiaoyuan Cheng, Haoyu Wang, Wenxuan Yuan, Ziyan Wang and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 26

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AI panel: 12 of 20 reviewers recommend it
lenient 2/5
medium 9/10
strict 1/5
74%Highly rated
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NeurIPS 2026UCLAaltoUniversity College LondonAlan Turing InstituteUncertainty & calibration

Conservative neural posterior estimation via distributionally robust training

DRO-NPE trains neural posterior estimators with distributionally robust worst-case losses to reduce overconfidence and improve calibration under limited simulation budgets.

William Laplante, Yuga Hikida, Charita Dellaporta, Francois-Xavier Briol and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 9 of 20 reviewers recommend it
lenient 3/5
medium 6/10
strict 0/5
86%Must read
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NeurIPS 2026University College London, UniveUCLCIVNanyang TechnologicalShanghai University of Finance aAdversarial robustness

Outlier-robust Diffusion Posterior Sampling for Bayesian Inverse Problems

Robust diffusion posterior sampling mitigates outlier-induced likelihood misspecification in diffusion-based Bayesian inverse problems with provable stability and consistent empirical gains.

Yiming Yang, Xiaoyuan Cheng, Yi He, Kaiyu Li and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · 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 5/5
medium 8/10
strict 1/5