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Showing papers from The Wharton School, University of Pennsylvania Show all papers

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NeurIPS 2026The Wharton School, University oWharton School, University of PeU PennsylvaniaBandits & online learning

Learning to target with network interference

Adaptive targeting under sparse network interference achieves near-optimal regret depending on structural knowledge, proving standard linear bandits are inefficient and offering practical algorithms.

Xiaomeng Wang, Hamsa Bastani, Osbert Bastani, Zhimei Ren

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026PekingThe Wharton School, University oFlow matching

TracingFlow: A Simulation-Free Trajectory Inference Framework Based on Second-Order Dynamics

TracingFlow is a simulation-free flow matching framework using second-order dynamics and acceleration fields to infer continuous system evolution from sparse snapshots, achieving superior trajectory and distribution accuracy.

Yuhao Sun, Zekun Wu, Zixun Huang, Peijie Zhou

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

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NeurIPS 2026The Wharton School, University oWharton Statistics, AmazoU PennsylvaniaUpennTraining dynamics

Estimating Implicit Regularization in Deep Learning

Gradient matching methods empirically estimate implicit regularization in deep networks, recovering explicit penalties and revealing dropout's implicit L2 effects.

Joseph H Rudoler, Kevin Tan, Giles Hooker, Konrad Kording

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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