45%Niche pick?Niche pickVote to see the scoreNeurIPS 2026Politecnico Milan, University ofNational University of SingaporeU MilanU IowaLearning theoryStrongly Adaptive Online Learning with Time-Varying Movement CostAndrew Jacobsen, Hao Qiu, Emmanuel Esposito, Mengxiao ZhangParis Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026– ReadersNo votes yet0/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 0 of 20 reviewers recommend itlenient 0/5medium 0/10strict 0/5
57%Worth a look?Worth a lookVote to see the scoreNeurIPS 2026U IowaClinical NLP & EHRDiffRisk: Diffusion Representation Learning with Informative Missingness for Health Risk PredictionShailesh Dahal, Ratri Mukherjee, Nicholas Mathews, Kishlay JhaSydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026– ReadersNo votes yet1/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 1 of 20 reviewers recommend itlenient 1/5medium 0/10strict 0/5
76%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026Tel AvivU IowaTel Aviv University / GoogleLearning theoryNear-Optimal Stochastic Linear Bandits with DelayStochastic linear bandits with delayed feedback yield near-optimal, dimension-free additive penalties for loss-independent delays but dimension-dependent penalties for loss-dependent delays, unlike multi-armed bandits.Ofir Schlisselberg, Mengxiao Zhang, Yishay MansourParis Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026– ReadersNo votes yet10/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 10 of 20 reviewers recommend itlenient 2/5medium 4/10strict 4/5
72%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026U IowaOptimizationPenalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level ProblemsPenalty-based first-order methods solve bilevel minimax optimization without lower-level strong convexity, achieving O(ε^-4) deterministic and O(ε^-9) stochastic complexity.Yiyang Shen, Yutian He, Weiran Wang, Qihang LinAtlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026– ReadersNo votes yet8/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 8 of 20 reviewers recommend itlenient 2/5medium 5/10strict 1/5