09.11.2026
53 papers at NeurIPS 2026 accepted
At this year's NeurIPS conference, 53 papers were accepted from researchers in our Cluster.
The 40th Conference on Neural Information Processing Systems (NeurIPS) is held at the Sydney International Convention Center in Australia from December 6-12, 2026, at the Georgia World Congress Center in Atlanta, United States from December 9-13, 2026 and at the Palais des Congrès de Paris in France from December 9-13, 2026. NeurIPS is the largest conference on machine learning and computational neuroscience. The purpose of the annual meetings is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field.
This year our Cluster is represented with 53 papers at NeurIPS.
List of titles of the papers by our members (bold) and their team members (check out the rest of the accepted papers here):
- Reaching a Consensus in Predictive Loops
Jiduan Wu, Rediet Abebe, Celestine Mendler-Dünner - MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models
Lukas Thede, Yash Kumar, David Chen, Danielle Bitterman, Matthias Bethge, Tom Hartvigsen, Zeynep Akata - SignalBench: Comparing Dense Feedback Methods for Long-Horizon Agents
Sergio Hernández-Gutiérrez, Matteo Merler, Ilze Amanda Auzina, Joschka Strüber, Ameya Prabhu, Matthias Bethge - DataComp-VLM: Improved open datasets for Vision-Language Models
Matteo Farina, Vishaal Udandarao, Thao Nguyen, Selim Kuzucu, Maximilian Böther, Andreas Hochlehnert, Adhiraj Ghosh, Marianna Nezhurina, Karsten Roth, Joschka Strüber, Yuhui Zhang, Sebastian Dziadzio, Elaine Sui, Soumya S Jahagirdar, Dhruba Ghosh, Hasan Hammoud, Thomas De Min, Simone Caldarella, Muhammad Jehanzeb Mirza, Sedrick Keh, Mehdi Cherti, Hilde Kuehne, Bernt Schiele, Serena Yeung-Levy, Muhammad Ferjad Naeem, Federico Tombari, Ana Klimovic, Elisa Ricci, Matthias Bethge, Sewoong Oh, Ameya Prabhu, Alessio Tonioni, Jenia Jitsev, Massimiliano Mancini, Ludwig Schmidt, Nikhil Parthasarathy - Evaluating Neural Data Tokenizers: A Framework for Assessing Learned Representations of Spiking Activity
Federico D'Agostino, Alex Gilbert, Susanne Keller, Jaivardhan Kapoor, Nicolas Reategui, Vedang Lad, Matthias Kümmerer, Jai Bhagat, Emin Orhan, Hasan A Bedel, Nikos Karantzas, Fabian Sinz, Surya Ganguli, Jakob H Macke, Sophia Sanborn, Alexander Ecker, Katrin Franke, Matthias Bethge, Andreas Tolias, Konstantin Willeke - LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-training
Andreas Hochlehnert, Marianna Nezhurina, Mehdi Cherti, Andrej Radonjic, Thaddäus Wiedemer, Christoph Schuhmann, Romain Beaumont, Wieland Brendel, Bernhard Schölkopf, A. Sophia Koepke, Jenia Jitsev, Matthias Bethge - RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments
Babak Rahmani, Sebastian Dziadzio, Joschka Strüber, Sergio Hernández-Gutiérrez, Matthias Bethge - Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping
Hsun-Yu Kuo, El Mahdi Chayti, Patrik Reizinger, Wieland Brendel, Martin Jaggi - LittleLearner: Language Models Under Pedagogically-Controlled Knowledge Exposure
Fanfei Li, Jana Zeller, Manuel Prada-Corral, Thaddäus Wiedemer, Prasanna Mayilvahanan, Ryan Cotterell, Wieland Brendel - Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan, Eric Schulz - Fast Wasserstein rates for estimating probability distributions of probabilistic graphical models
Daniel Bartl, Stephan Eckstein - From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures
Florian Rottach, William Rudman, Bastian Rieck, Harrisen Scells, Carsten Eickhoff - Understanding Multi-View Transformers
Julien Gaubil, Michal Stary, Louis Martinez, Andreas Geiger, Maks Ovsjanikov, Ayush Tewari, Vincent Sitzmann - FutureSim: Replaying World Events to Evaluate Adaptive Agents
Shashwat Goel, Nikhil Chandak, Arvindh Arun, Ameya Prabhu, Steffen Staab, Moritz Hardt, Maksym Andriushchenko, Jonas Geiping - Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs
Naman Deep Singh, Maximilian Mueller, Amit Peleg, Francesco Croce, Matthias Hein - Learning POMDP World Models from Observations with Language-Model Priors
Valentin Six, Frederik Panse, Mathis Fajeau, Lancelot Da Costa, Mridul Sharma, Alfonso Amayuelas, Tim Xiao, David Hyland, Philipp Hennig, Bernhard Schölkopf - Conditioning Gaussian Processes on Almost Anything
Henry Moss, Lachlan Astfalck, Tom Cowperthwaite, Colin Doumont, Samuel Willis, Christopher Nemeth, Philipp Hennig, Andrew Zammit-Mangion - Closed-Form Last Layer Optimization
Alexandre Galashov, Nathaël Da Costa, Liyuan Xu, Philipp Hennig, Arthur Gretton - BO-Arena: An Evolving Benchmark for High-Dimensional Bayesian Optimisation
Thomas Christie, Colin Doumont, Henry Moss, Philipp Hennig - Beyond IID: How General Are Tabular Foundation Models, Really?
Lennart Purucker, Andrej Tschalzev, Nick Erickson, David Holzmüller, Alan Arazi, Alexander Pfefferle, Mustafa Tajjar, Gioia Blayer, Gael Varoquaux, Frank Hutter - Understanding the Surprising Generalization Properties of Tabular Foundation Models
Nour Shaheen, Junwei (Jeremy) Ma, Alex Labach, Frank Hutter, Anthony L Caterini, Valentin Thomas - Encoding RNA Topology into Synthetic Alignments for 3D Structure Prediction
Jörg Franke, Iris K Tennie, Michael Uhl, Ryan Koksal, Dominika Matus, Dominik Scheuer, Frank Hutter, Rolf Backofen, Frederic Runge - STRABLE: Benchmarking Tabular Machine Learning with Strings
Gioia Blayer, Myung Jun Kim, Félix Lefebvre, Lennart Purucker, Alan Arazi, Eilam Shapira, Roi Reichart, Frank Hutter, Marine Le Morvan, David Holzmüller, Gael Varoquaux - When is Warmstarting Effective for Scaling Language Models?
Neeratyoy Mallik, Maciej Janowski, Johannes Hog, Herilalaina Rakotoarison, Josif Grabocka, Frank Hutter, Aaron Klein - MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Alan Arazi, Eilam Shapira, Shoham Grunblat, Mor Ventura, Elad Hoffer, Gioia Blayer, David Holzmüller, Lennart Purucker, Gael Varoquaux, Frank Hutter, Roi Reichart - TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
Vladyslav Moroshan, Julien Siems, Arber Zela, Timur Carstensen, Frank Hutter - AI Construct Lexis: An Ontology of the Hidden Assumptions in AI Evaluation
Olawale Salaudeen, Florian E. Dorner, Tom Sühr, Sang Truong, Haoran Zhang, Patrick Bissett, James Fox, Marzyeh Ghassemi, Tom Hartvigsen, Peter Hase, Augustin Kelava, Suhas Mahesh, Margaret Mitchell, Jamelle Watson-Daniels, Sanmi Koyejo, Angelina Wang - FalconPerception-HD: High Density Perception via Reinforcement Learning
Sofian Chaybouti, YASSER ABDELAZIZ DAHOU DJILALI, Ngoc D Huynh, REDA ALAMI, Hilde Kuehne - When Trackers Fail: VLM-Guided Verification and Recovery for Robust Video Object Segmentation
Valay Mahesh Bundele, Susmit Agrawal, Mehran Hosseinzadeh, The Nam Nguyen, Hendrik PA Lensch - Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons
Aaron Spieler, Georg Martius, Anna Levina - Soft Forward-Backward Representations for Zero-shot Reinforcement Learning with General Utilities
Marco Bagatella, Thomas Rupf, Georg Martius, Andreas Krause - Drifting Fields are not Conservative
Leonard T. Franz, Sebastian Hoffmann, Tim Weiland, Bernhard Schölkopf, Georg Martius - SAMoR: Motion Modelling for Articulated Objects of Any Skeleton and Topology
Yuhao Zhang, Gerard Pons-Moll, Tolga Birdal - CoTs as Probabilistic Programs: A Programmatic View of Thinking Step-by-Step in Language Models
Kyle Richardson, Yu Feng, Poorva Garg, Junyan Cheng, Dan Roth, Guy Van den Broeck - Operads for compositional reasoning in LLMs
Nathaniel Bottman, Yinhong Liu, Kyle Richardson - Controlling for Omitted Variable Bias in Deep Neural Networks
Manuel Pfeuffer, Roshan P Rane, Kerstin Ritter, Sonja Greven - Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation
Erik Großkopf, Soumya Snigdha Kundu, Hendrik Möller, Nicolas Münster, Mehdi Astaraki, Paula T Buzduga, Kerstin Ritter, Benedikt Wiestler, Jan Kirschke, Jonathan Shapey, Tom Vercauteren, Florian Kofler - Flow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics
Sam Gijsen, Michał Łukomski, Marc-Andre Schulz, Kerstin Ritter - Foveated BagNet: Inherent Interpretability Does Not Exclude Global Context
Holger Heidrich, Sarah Müller, Andreas Schilling - PENEX: AdaBoost-Inspired Neural Network Regularization
Klaus-Rudolf Kladny, Bernhard Schölkopf, Michael Muehlebach - PVFormer: Proper Velocity Transformer for Stable and Scalable Hyperbolic Representation Learning
Xianglong Shi, Nicu Sebe, Bernhard Schölkopf, Ziheng Chen - Deriving Hyperparameter Scaling Laws via Modern Optimization Theory
Egor Shulgin, Jörg Franke, Dimitri von Rütte, Tianyue Zhang, Niccolò Ajroldi, Korbinian Pöppel, Bernhard Schölkopf, Aaron Klein, Peter Richtarik, Antonio Orvieto - SuperSycophantic: Stress-Testing Frontier LLMs from Single- to Multi-Turn Sycophancy
Terry J. C. Zhang, Oscar S Yasunaga, Wenyuan Jiang, Jessica Bo, Florent Draye, Aydin Javadov, Arth Singh, Lucille Muir, VICTORIA OLDEMBURGO DE MELLO, Bernhard Schölkopf, Zhijing Jin - AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment
Eric Zhang, Terry J. C. Zhang, Chijioke Ugwuanyi, Jerick Shi, Bernhard Schölkopf, Zhijing Jin - GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
Pepijn Cobben, Xuanqiang A Huang, Thao Pham, Isabel Dahlgren, Bernhard Schölkopf, Terry J. C. Zhang, Zhijing Jin - DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision
Wenlin Chen, Mingtian Zhang, Jiajun He, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber - Perception for Action in Latent World Models
Petr Ivashkov, Randall Balestriero, Bernhard Schölkopf - The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions
Alejandro H. Artiles, Martin Weiss, Levin Brinkmann, Iyad Rahwan, Bernhard Schölkopf, Chris Pal, Hugo Larochelle, Anirudh Goyal, Nasim Rahaman - Causality can systematically address the monsters under the bench(marks)
Felix Leeb, Zhijing Jin, Bernhard Schölkopf - Umbilic Multinomial Logistic Regression
Zihan Su, Nicu Sebe, Bernhard Schölkopf, Ziheng Chen - Intrinsically Interpretable Attention via Sparse Post-Training
Florent Draye, Anson Lei, Hsiao-Ru Pan, Ingmar Posner, Bernhard Schölkopf - Transferability for General Reasoning: An Automated Curriculum for Multi-Domain LLM RL
Yongjin Yang, Jiarui Liu, Yinghui He, Lechen Zhang, Bernhard Schölkopf, Zhijing Jin - Corruptions of Supervised Learning Problems: Typology and Mitigations
Laura Iacovissi, Nan Lu, Robert Williamson