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Showing papers from Humboldt Universität Berlin Show all papers

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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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NeurIPS 2026East China University of ScienceU PaduaHumboldt Universität BerlinMulti-agent RL

Submodular Multi-Agent Reinforcement Learning for Effective Online Distributed Task Allocation

Jing Liu, Yangyang YANG, Luca Ballotta, Fangfei Li and 2 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Humboldt Universität zu BerlinHumboldt Universität BerlinEberhard-Karls-Universität TübinHumboldt-Universität zu BerlinFairness & bias

Controlling for Omitted Variable Bias in Deep Neural Networks

A control-variable method using generalized additive modeling and cross-fitted ridge refitting removes omitted-variable bias from deep networks, yielding unbiased predictions.

Manuel Pfeuffer, Roshan P Rane, Kerstin Ritter, Sonja Greven

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5
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NeurIPS 2026Technische Universität BerlinHumboldt Universität BerlinLearning theory

Resilient Byzantine Agreement with Predictions

Byzantine agreement with predictors achieves tight consistency-robustness trade-offs and linear resilience degradation with prediction errors.

Julien Dallot, Darya Melnyk, Tijana Milentijević, Stefan Schmid and 1 more

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

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AI panel: 5 of 20 reviewers recommend it
lenient 0/5
medium 4/10
strict 1/5