Graepel, Thore

60 publications

NeurIPS 2025 PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, Błażej Osiński, Ridvan Eksi, Zichao Yan, Rory Stark, Kun Zhang, Thore Graepel

NeurIPSW 2024 PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, Błażej Osiński, Ridvan Eksi, Kun Zhang, Thore Graepel

ICLR 2022 EigenGame Unloaded: When Playing Games Is Better than Optimizing Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel

ICLRW 2022 EigenGame Unloaded: When Playing Games Is Better than Optimizing Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel

ICLRW 2022 Learning Truthful, Efficient, and Welfare Maximizing Auction Rules Andrea Tacchetti, Dj Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach

ICLR 2022 NeuPL: Neural Population Learning Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel

IJCAI 2021 A Neural Network Auction for Group Decision Making over a Continuous Space Yoram Bachrach, Ian Gemp, Marta Garnelo, János Kramár, Tom Eccles, Dan Rosenbaum, Thore Graepel

ICLR 2021 EigenGame: PCA as a Nash Equilibrium Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel

JAIR 2021 Game Plan: What AI Can Do for Football, and What Football Can Do for AI Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis

ICML 2021 Multi-Agent Training Beyond Zero-Sum with Correlated Equilibrium Meta-Solvers Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel

ICML 2021 Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot Joel Z Leibo, Edgar A Dueñez-Guzman, Alexander Vezhnevets, John P Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel

ICLR 2020 A Generalized Training Approach for Multiagent Learning Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos

NeurIPS 2020 Learning to Play No-Press Diplomacy with Best Response Policy Iteration Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas Hudson, Nicolas Porcel, Marc Lanctot, Julien Perolat, Richard Everett, Satinder P. Singh, Thore Graepel, Yoram Bachrach

ICLR 2020 Smooth Markets: A Basic Mechanism for Organizing Gradient-Based Learners David Balduzzi, Wojciech M Czarnecki, Thomas W Anthony, Ian M Gemp, Edward Hughes, Joel Z Leibo, Georgios Piliouras, Thore Graepel

NeurIPS 2019 Biases for Emergent Communication in Multi-Agent Reinforcement Learning Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel

JMLR 2019 Differentiable Game Mechanics Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

ICLR 2019 Emergent Coordination Through Competition Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel

ICML 2019 Open-Ended Learning in Symmetric Zero-Sum Games David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel

ICLR 2019 Relational Forward Models for Multi-Agent Learning Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia

NeurIPS 2018 Inequity Aversion Improves Cooperation in Intertemporal Social Dilemmas Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueñez-Guzman, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, Heather Roff, Thore Graepel

NeurIPS 2018 Re-Evaluating Evaluation David Balduzzi, Karl Tuyls, Julien Perolat, Thore Graepel

ICML 2018 The Mechanics of N-Player Differentiable Games David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

NeurIPS 2017 A Multi-Agent Reinforcement Learning Model of Common-Pool Resource Appropriation Julien Pérolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel

NeurIPS 2017 A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel

MLJ 2014 Manifestations of User Personality in Website Choice and Behaviour on Online Social Networks Michal Kosinski, Yoram Bachrach, Pushmeet Kohli, David Stillwell, Thore Graepel

ICML 2012 How to Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing Yoram Bachrach, Thore Graepel, Tom Minka, John Guiver

AISTATS 2012 Kernel Topic Models Philipp Hennig, David Stern, Ralf Herbrich, Thore Graepel

AAAI 2012 Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests Xi Alice Gao, Yoram Bachrach, Peter B. Key, Thore Graepel

ECML-PKDD 2012 Score-Based Bayesian Skill Learning Shengbo Guo, Scott Sanner, Thore Graepel, Wray L. Buntine

ECML-PKDD 2010 Bayesian Knowledge Corroboration with Logical Rules and User Feedback Gjergji Kasneci, Jurgen Van Gael, Ralf Herbrich, Thore Graepel

AISTATS 2010 Bayesian Online Learning for Multi-Label and Multi-Variate Performance Measures Xinhua Zhang, Thore Graepel, Ralf Herbrich

AISTATS 2010 Coherent Inference on Optimal Play in Game Trees Philipp Hennig, David Stern, Thore Graepel

AAAI 2010 Collaborative Expert Portfolio Management David H. Stern, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, Armando Tacchella

ICML 2010 Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich

ICML 2007 Learning to Solve Game Trees David H. Stern, Ralf Herbrich, Thore Graepel

NeurIPS 2007 TrueSkill Through Time: Revisiting the History of Chess Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel

ICML 2006 Bayesian Pattern Ranking for Move Prediction in the Game of Go David H. Stern, Ralf Herbrich, Thore Graepel

MLJ 2006 Machine Learning and Games Michael H. Bowling, Johannes Fürnkranz, Thore Graepel, Ron Musick

NeurIPS 2006 TrueSkill™: A Bayesian Skill Rating System Ralf Herbrich, Tom Minka, Thore Graepel

JMLR 2005 Generalization Bounds for the Area Under the ROC Curve Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth

MLJ 2005 PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification Thore Graepel, Ralf Herbrich, John Shawe-Taylor

AISTATS 2005 Poisson-Networks: A Model for Structured Poisson Processes Shyamsundar Rajaram, Thore Graepel, Ralf Herbrich

NeurIPS 2004 A Large Deviation Bound for the Area Under the ROC Curve Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth

NeurIPS 2004 Modelling Uncertainty in the Game of Go David H. Stern, Thore Graepel, David MacKay

AISTATS 2003 Combining Conjugate Direction Methods with Stochastic Approximation of Gradients Nicol N. Schraudolph, Thore Graepel

NeurIPS 2003 Invariant Pattern Recognition by Semi-Definite Programming Machines Thore Graepel, Ralf Herbrich

COLT 2003 Reducing Kernel Matrix Diagonal Dominance Using Semi-Definite Programming Jaz S. Kandola, Thore Graepel, John Shawe-Taylor

NeurIPS 2003 Semi-Definite Programming by Perceptron Learning Thore Graepel, Ralf Herbrich, Andriy Kharechko, John S. Shawe-taylor

ICML 2003 Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations Thore Graepel

JMLR 2001 Bayes Point Machines (Kernel Machines Section) Ralf Herbrich, Thore Graepel, Colin Campbell

NeurIPS 2000 A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs Work Ralf Herbrich, Thore Graepel

NeurIPS 2000 From Margin to Sparsity Thore Graepel, Ralf Herbrich, Robert C. Williamson

COLT 2000 Generalisation Error Bounds for Sparse Linear Classifiers Thore Graepel, Ralf Herbrich, John Shawe-Taylor

NeurIPS 2000 Large Scale Bayes Point Machines Ralf Herbrich, Thore Graepel

COLT 2000 Sparsity vs. Large Margins for Linear Classifiers Ralf Herbrich, Thore Graepel, John Shawe-Taylor

NeurIPS 2000 The Kernel Gibbs Sampler Thore Graepel, Ralf Herbrich

NeCo 1999 A Stochastic Self-Organizing mAP for Proximity Data Thore Graepel, Klaus Obermayer

NeurIPS 1999 Bayesian Transduction Thore Graepel, Ralf Herbrich, Klaus Obermayer

NeurIPS 1998 Classification on Pairwise Proximity Data Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer

NeurIPS 1997 An Annealed Self-Organizing mAP for Source Channel Coding Matthias Burger, Thore Graepel, Klaus Obermayer