45%Niche pick?Niche pickVote to see the scoreNeurIPS 2026SpotlightU Wisconsin-MadisonUniversity Of Wisconsin MadisonUCSDComputer Science and EngineeringU California San DiegoLearning theory(Strongly) Replicable Distribution Testers imply High Probability Distribution TestersIlias Diakonikolas, Jingyi Gao, Daniel Kane, Sihan Liu and 1 moreAtlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · 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
67%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026U California, IrvineComputer Science and EngineeringYaleUCIGenomics & single-cellsMMC-22M: A Context-Aware Dataset and Benchmark for Single-Cell Spatial TranscriptomicsXi Li, Yaqi Hu, Ziheng Duan, Xinyi Wang and 4 moreAtlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026– ReadersNo votes yet2/20 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: 2 of 20 reviewers recommend itlenient 2/5medium 0/10strict 0/5
76%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026U California, San DiegoHarvard University, HarvardComputer Science and EngineeringUCSDExplainable AIMeasuring What Matters: Synthetic Benchmarks for Concept Bottleneck ModelsSynthetic benchmarks for concept bottleneck models generate controlled labeled datasets to evaluate decision support and automation use cases, diagnose failure modes, and guide testing.Julian Skirzynski, Harry Cheon, Shreyas Kadekodi, Meredith Stewart and 1 moreSydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · 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 5/5medium 4/10strict 1/5
72%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026SpotlightU Wisconsin-MadisonU Wisconsin - MadisonUCSDComputer Science and EngineeringLearning theoryTestable Learning of General Halfspaces under Massart NoiseA testable learning algorithm learns general Massart halfspaces under Gaussian marginals with quasi-polynomial complexity matching SQ lower bounds.Ilias Diakonikolas, Giannis Iakovidis, Daniel Kane, Sihan LiuAtlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 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 4/10strict 2/5