Phase Transitions in Soft Topographic Vector Quantization. - Matthias Burger; Thore Graepel; Klaus Obermayer (ICANN, 1997)

An Annealed Self-Organizing Map for Source Channel Coding. - Matthias Burger; Thore Graepel; Klaus Obermayer (NIPS, 1997)

Classification on Pairwise Proximity Data. - Thore Graepel; Ralf Herbrich; Peter Bollmann-Sdorra; Klaus Obermayer (NIPS, 1998)

Self-organizing maps: Generalizations and new optimization techniques. - Thore Graepel; Matthias Burger; Klaus Obermayer (Neurocomputing, 1998)

Bayesian Transduction. - Thore Graepel; Ralf Herbrich; Klaus Obermayer (NIPS, 1999)

A Stochastic Self-Organizing Map for Proximity Data. - Thore Graepel; Klaus Obermayer (Neural Comput., 1999)

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

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

Gaussian Process Regression: Active Data Selection and Test Point Rejection. - Sambu Seo; Marko Wallat; Thore Graepel; Klaus Obermayer (DAGM-Symposium, 2000)

Robust Bayes Point Machines. - Ralf Herbrich; Thore Graepel; Colin Campbell (ESANN, 2000)

The Kernel Gibbs Sampler. - Thore Graepel; Ralf Herbrich (NIPS, 2000)

From Margin to Sparsity. - Thore Graepel; Ralf Herbrich; Robert C. Williamson (NIPS, 2000)

A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. - Ralf Herbrich; Thore Graepel (NIPS, 2000)

Large Scale Bayes Point Machines. - Ralf Herbrich; Thore Graepel (NIPS, 2000)

Learning on Graphs in the Game of Go. - Thore Graepel; Mike Goutrié; Marco Krüger; Ralf Herbrich (ICANN, 2001)

Bayes Point Machines. - Ralf Herbrich; Thore Graepel; Colin Campbell (J. Mach. Learn. Res., 2001)

Kernel Matrix Completion by Semidefinite Programming. - Thore Graepel (ICANN, 2002)

Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems. - Thore Graepel; Nicol N. Schraudolph (ICANN, 2002)

Conjugate Directions for Stochastic Gradient Descent. - Nicol N. Schraudolph; Thore Graepel (ICANN, 2002)

Kernel Methods for Document Filtering. - Nicola Cancedda; Cyril Goutte; Jean-Michel Renders; Nicolò Cesa-Bianchi; Alex Conconi; Yaoyong Li; John Shawe-Taylor; Alexei Vinokourov; Thore Graepel; Claudio Gentile (TREC, 2002)

A PAC-Bayesian margin bound for linear classifiers. - Ralf Herbrich; Thore Graepel (IEEE Trans. Inf. Theory, 2002)

PAC-Bayesian pattern classification with kernels: theory, algorithms, and an application to the game of Go. - Thore Graepel (2002)

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

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

Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations. - Thore Graepel (ICML, 2003)

Invariant Pattern Recognition by Semi-Definite Programming Machines. - Thore Graepel; Ralf Herbrich (NIPS, 2003)

Semi-Definite Programming by Perceptron Learning. - Thore Graepel; Ralf Herbrich; Andriy Kharechko; John Shawe-Taylor (NIPS, 2003)

Introduction to the Special Issue on Learning Theory. - Ralf Herbrich; Thore Graepel (J. Mach. Learn. Res., 2003)

A Large Deviation Bound for the Area Under the ROC Curve. - Shivani Agarwal 001; Thore Graepel; Ralf Herbrich; Dan Roth 001 (NIPS, 2004)

Modelling Uncertainty in the Game of Go. - David H. Stern; Thore Graepel; David J. C. MacKay (NIPS, 2004)

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

Generalization Bounds for the Area Under the ROC Curve. - Shivani Agarwal 001; Thore Graepel; Ralf Herbrich; Sariel Har-Peled; Dan Roth 001 (J. Mach. Learn. Res., 2005)

PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. - Thore Graepel; Ralf Herbrich; John Shawe-Taylor (Mach. Learn., 2005)

Bayesian pattern ranking for move prediction in the game of Go. - David H. Stern; Ralf Herbrich; Thore Graepel (ICML, 2006)

TrueSkillTM: A Bayesian Skill Rating System. - Ralf Herbrich; Tom Minka; Thore Graepel (NIPS, 2006)

Machine learning and games. - Michael H. Bowling; Johannes Fürnkranz; Thore Graepel; Ron Musick (Mach. Learn., 2006)

Learning to solve game trees. - David H. Stern; Ralf Herbrich; Thore Graepel (ICML, 2007)

TrueSkill Through Time: Revisiting the History of Chess. - Pierre Dangauthier; Ralf Herbrich; Tom Minka; Thore Graepel (NIPS, 2007)

Large scale data analysis and modelling in online services and advertising. - Thore Graepel; Ralf Herbrich (KDD, 2008)

Scalable clustering and keyword suggestion for online advertisements. - Anton Schwaighofer; Joaquin Quiñonero Candela; Thomas Borchert; Thore Graepel; Ralf Herbrich (KDD Workshop on Data Mining and Audience Intelligence for Advertising, 2009)

Matchbox: large scale online bayesian recommendations. - David H. Stern; Ralf Herbrich; Thore Graepel (WWW, 2009)

Novel tools to streamline the conference review process: experiences from SIGKDD'09. - Peter A. Flach; Sebastian Spiegler; Bruno Golénia; Simon Price; John Guiver; Ralf Herbrich; Thore Graepel; Mohammed J. Zaki (SIGKDD Explor., 2009)

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

Bayesian Knowledge Corroboration with Logical Rules and User Feedback. - Gjergji Kasneci; Jurgen Van Gael; Ralf Herbrich; Thore Graepel (ECML/PKDD (2), 2010)

Coherent Inference on Optimal Play in Game Trees. - Philipp Hennig; David H. Stern; Thore Graepel (AISTATS, 2010)

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. - Xinhua Zhang; Thore Graepel; Ralf Herbrich (AISTATS, 2010)

Rip-off: playing the cooperative negotiation game. - Yoram Bachrach; Pushmeet Kohli; Thore Graepel (AAMAS, 2011)

DBrev: Dreaming of a Database Revolution. - Gjergji Kasneci; Jurgen Van Gael; Thore Graepel (CIDR, 2011)

Automated feature generation from structured knowledge. - Weiwei Cheng; Gjergji Kasneci; Thore Graepel; David H. Stern; Ralf Herbrich (CIKM, 2011)

Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model. - Scott Sanner; Shengbo Guo; Thore Graepel; Sadegh Kharazmi; Sarvnaz Karimi (CIKM, 2011)

Sociable killers: understanding social relationships in an online first-person shooter game. - Yan Xu 011; Xiang Cao; Abigail Sellen; Ralf Herbrich; Thore Graepel (CSCW, 2011)

CoBayes: bayesian knowledge corroboration with assessors of unknown areas of expertise. - Gjergji Kasneci; Jurgen Van Gael; David H. Stern; Thore Graepel (WSDM, 2011)

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

Crowd IQ: aggregating opinions to boost performance. - Yoram Bachrach; Thore Graepel; Gjergji Kasneci; Michal Kosinski; Jurgen Van Gael (AAMAS, 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 (ICML, 2012)

Score-Based Bayesian Skill Learning. - Shengbo Guo; Scott Sanner; Thore Graepel; Wray L. Buntine (ECML/PKDD (1), 2012)

Collaborative learning of preference rankings. - Tim Salimans; Ulrich Paquet; Thore Graepel (RecSys, 2012)

Personality and patterns of Facebook usage. - Yoram Bachrach; Michal Kosinski; Thore Graepel; Pushmeet Kohli; David Stillwell (WebSci, 2012)

Colonel Blotto on Facebook: the effect of social relations on strategic interaction. - Pushmeet Kohli; Michael J. Kearns; Yoram Bachrach; Ralf Herbrich; David Stillwell; Thore Graepel (WebSci, 2012)

Crowd IQ: measuring the intelligence of crowdsourcing platforms. - Michal Kosinski; Yoram Bachrach; Gjergji Kasneci; Jurgen Van Gael; Thore Graepel (WebSci, 2012)

Compiling Relational Database Schemata into Probabilistic Graphical Models - Sameer Singh 001; Thore Graepel (CoRR, 2012)

ML Confidential: Machine Learning on Encrypted Data. - Thore Graepel; Kristin E. Lauter; Michael Naehrig (IACR Cryptol. ePrint Arch., 2012)

Kernel Topic Models. - Philipp Hennig; David H. Stern; Ralf Herbrich; Thore Graepel (AISTATS, 2012)

Automated probabilistic modeling for relational data. - Sameer Singh 001; Thore Graepel (CIKM, 2013)

SIGMa: simple greedy matching for aligning large knowledge bases. - Simon Lacoste-Julien; Konstantina Palla; Alex Davies; Gjergji Kasneci; Thore Graepel; Zoubin Ghahramani (KDD, 2013)

A model-learner pattern for bayesian reasoning. - Andrew D. Gordon 001; Mihhail Aizatulin; Johannes Borgström; Guillaume Claret; Thore Graepel; Aditya V. Nori; Sriram K. Rajamani; Claudio V. Russo (POPL, 2013)

Inferring the demographics of search users: social data meets search queries. - Bin Bi; Milad Shokouhi; Michal Kosinski; Thore Graepel (WWW, 2013)

Your digital image: factors behind demographic and psychometric predictions from social network profiles. - Yoram Bachrach; Thore Graepel; Pushmeet Kohli; Michal Kosinski; David Stillwell (AAMAS, 2014)

Learning a Theory of Marriage (and Other Relations) from a Web Corpus. - Sandro Bauer; Stephen Clark; Laura Rimell; Thore Graepel (ECIR, 2014)

Tabular: a schema-driven probabilistic programming language. - Andrew D. Gordon 001; Thore Graepel; Nicolas Rolland; Claudio V. Russo; Johannes Borgström; John Guiver (POPL, 2014)

Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles. - Sandro Bauer; Stephen Clark; Thore Graepel (SocInfo Workshops, 2014)

A Comparison of learning algorithms on the Arcade Learning Environment. - Aaron Defazio; Thore Graepel (CoRR, 2014)

Manifestations of user personality in website choice and behaviour on online social networks. - Michal Kosinski; Yoram Bachrach; Pushmeet Kohli; David Stillwell; Thore Graepel (Mach. Learn., 2014)

Probabilistic Programs as Spreadsheet Queries. - Andrew D. Gordon 001; Claudio V. Russo; Marcin Szymczak 002; Johannes Borgström; Nicolas Rolland; Thore Graepel; Daniel Tarlow (ESOP, 2015)

The Wreath Process: A totally generative model of geometric shape based on nested symmetries. - Diana Borsa; Thore Graepel; Andrew D. Gordon 001 (CoRR, 2015)

Learning Shared Representations in Multi-task Reinforcement Learning. - Diana Borsa; Thore Graepel; John Shawe-Taylor (CoRR, 2016)

Mastering the game of Go with deep neural networks and tree search. - David Silver; Aja Huang; Chris J. Maddison; Arthur Guez; Laurent Sifre; George van den Driessche 002; Julian Schrittwieser; Ioannis Antonoglou; Vedavyas Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; et al. (Nat., 2016)

Multi-agent Reinforcement Learning in Sequential Social Dilemmas. - Joel Z. Leibo; Vinícius Flores Zambaldi; Marc Lanctot; Janusz Marecki; Thore Graepel (AAMAS, 2017)

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. - Marc Lanctot; Vinícius Flores Zambaldi; Audrunas Gruslys; Angeliki Lazaridou; Karl Tuyls; Julien Pérolat; David Silver; Thore Graepel (NIPS, 2017)

A multi-agent reinforcement learning model of common-pool resource appropriation. - Julien Pérolat; Joel Z. Leibo; Vinícius Flores Zambaldi; Charles Beattie; Karl Tuyls; Thore Graepel (NIPS, 2017)

Value-Decomposition Networks For Cooperative Multi-Agent Learning. - Peter Sunehag; Guy Lever; Audrunas Gruslys; Wojciech Marian Czarnecki; Vinícius Flores Zambaldi; Max Jaderberg; Marc Lanctot; Nicolas Sonnerat; Joel Z. Leibo; Karl Tuyls; Thore Graepel (CoRR, 2017)

Symmetric Decomposition of Asymmetric Games. - Karl Tuyls; Julien Pérolat; Marc Lanctot; Georg Ostrovski; Rahul Savani; Joel Z. Leibo; Toby Ord; Thore Graepel; Shane Legg (CoRR, 2017)

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. - David Silver; Thomas Hubert; Julian Schrittwieser; Ioannis Antonoglou; Matthew Lai; Arthur Guez; Marc Lanctot; Laurent Sifre; Dharshan Kumaran; Thore Graepel; Timothy P. Lillicrap; Karen Simonyan; et al. (CoRR, 2017)

Mastering the game of Go without human knowledge. - David Silver; Julian Schrittwieser; Karen Simonyan; Ioannis Antonoglou; Aja Huang; Arthur Guez; Thomas Hubert; Lucas Baker; Matthew Lai; Adrian Bolton; Yutian Chen 001; Timothy P. Lillicrap; et al. (Nat., 2017)

Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. - Peter Sunehag; Guy Lever; Audrunas Gruslys; Wojciech Marian Czarnecki; Vinícius Flores Zambaldi; Max Jaderberg; Marc Lanctot; Nicolas Sonnerat; Joel Z. Leibo; Karl Tuyls; Thore Graepel (AAMAS, 2018)

A Generalised Method for Empirical Game Theoretic Analysis. - Karl Tuyls; Julien Pérolat; Marc Lanctot; Joel Z. Leibo; Thore Graepel (AAMAS, 2018)

The Mechanics of n-Player Differentiable Games. - David Balduzzi; Sébastien Racanière; James Martens; Jakob N. Foerster; Karl Tuyls; Thore Graepel (ICML, 2018)

Re-evaluating evaluation. - David Balduzzi; Karl Tuyls; Julien Pérolat; Thore Graepel (NeurIPS, 2018)

Inequity aversion improves cooperation in intertemporal social dilemmas. - Edward Hughes 001; Joel Z. Leibo; Matthew Phillips; Karl Tuyls; Edgar A. Duéñez-Guzmán; Antonio García Castañeda; Iain Dunning; Tina Zhu; Kevin R. McKee; Raphael Koster; Heather Roff; Thore Graepel (NeurIPS, 2018)

Inequity aversion resolves intertemporal social dilemmas. - Edward Hughes 001; Joel Z. Leibo; Matthew G. Philips; Karl Tuyls; Edgar A. Duéñez-Guzmán; Antonio García Castañeda; Iain Dunning; Tina Zhu; Kevin R. McKee; Raphael Koster; Heather Roff; Thore Graepel (CoRR, 2018)

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning. - Max Jaderberg; Wojciech M. Czarnecki; Iain Dunning; Luke Marris; Guy Lever; Antonio García Castañeda; Charles Beattie; Neil C. Rabinowitz; Ari S. Morcos; Avraham Ruderman; Nicolas Sonnerat; Tim Green; et al. (CoRR, 2018)

Relational Forward Models for Multi-Agent Learning. - Andrea Tacchetti; H. Francis Song; Pedro A. M. Mediano; Vinícius Flores Zambaldi; Neil C. Rabinowitz; Thore Graepel; Matthew M. Botvinick; Peter W. Battaglia (CoRR, 2018)

The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution. - Dylan Banarse; Yoram Bachrach; Siqi Liu 002; Guy Lever; Nicolas Heess; Chrisantha Fernando; Pushmeet Kohli; Thore Graepel (AAMAS, 2019)

Malthusian Reinforcement Learning. - Joel Z. Leibo; Julien Pérolat; Edward Hughes 001; Steven Wheelwright; Adam H. Marblestone; Edgar A. Duéñez-Guzmán; Peter Sunehag; Iain Dunning; Thore Graepel (AAMAS, 2019)

Emergent Coordination Through Competition. - Siqi Liu 002; Guy Lever; Josh Merel; Saran Tunyasuvunakool; Nicolas Heess; Thore Graepel (ICLR (Poster), 2019)

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

Open-ended learning in symmetric zero-sum games. - David Balduzzi; Marta Garnelo; Yoram Bachrach; Wojciech Czarnecki 001; Julien Pérolat; Max Jaderberg; Thore Graepel (ICML, 2019)

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems. - Peter Sunehag; Guy Lever; Siqi Liu 002; Josh Merel; Nicolas Heess; Joel Z. Leibo; Edward Hughes 001; Tom Eccles; Thore Graepel (ALIFE, 2019)

Biases for Emergent Communication in Multi-agent Reinforcement Learning. - Tom Eccles; Yoram Bachrach; Guy Lever; Angeliki Lazaridou; Thore Graepel (NeurIPS, 2019)

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research. - Joel Z. Leibo; Edward Hughes 001; Marc Lanctot; Thore Graepel (CoRR, 2019)

A Neural Architecture for Designing Truthful and Efficient Auctions. - Andrea Tacchetti; DJ Strouse; Marta Garnelo; Thore Graepel; Yoram Bachrach (CoRR, 2019)

Differentiable Game Mechanics. - Alistair Letcher; David Balduzzi; Sébastien Racanière; James Martens; Jakob N. Foerster; Karl Tuyls; Thore Graepel (J. Mach. Learn. Res., 2019)

Tabular: Probabilistic Inference from the Spreadsheet. - Andrew D. Gordon 001; Claudio V. Russo; Marcin Szymczak 002; Johannes Borgström; Nicolas Rolland; Thore Graepel; Daniel Tarlow (Foundations of Probabilistic Programming, 2020)

Automatic Curricula in Deep Multi-Agent Reinforc ement Learning. - Thore Graepel (AAMAS, 2020)

Smooth markets: A basic mechanism for organizing gradient-based learners. - David Balduzzi; Wojciech M. Czarnecki; Tom Anthony 001; Ian Gemp; Edward Hughes 001; Joel Z. Leibo; Georgios Piliouras; Thore Graepel (ICLR, 2020)

A Generalized Training Approach for Multiagent Learning. - Paul Muller; Shayegan Omidshafiei; Mark Rowland 001; Karl Tuyls; Julien Pérolat; Siqi Liu 002; Daniel Hennes; Luke Marris; Marc Lanctot; Edward Hughes 001; Zhe Wang 055; Guy Lever; et al. (ICLR, 2020)

Adaptive Mechanism Design: Learning to Promote Cooperation. - Tobias Baumann; Thore Graepel; John Shawe-Taylor (IJCNN, 2020)

Learning to Play No-Press Diplomacy with Best Response Policy Iteration. - Thomas W. Anthony 001; Tom Eccles; Andrea Tacchetti; János Kramár; Ian Gemp; Thomas C. Hudson; Nicolas Porcel; Marc Lanctot; Julien Pérolat; Richard Everett 001; Satinder Singh 001; Thore Graepel; et al. (NeurIPS, 2020)

Bounds and dynamics for empirical game theoretic analysis. - Karl Tuyls; Julien Pérolat; Marc Lanctot; Edward Hughes 001; Richard Everett 001; Joel Z. Leibo; Csaba Szepesvári; Thore Graepel (Auton. Agents Multi Agent Syst., 2020)

Negotiating team formation using deep reinforcement learning. - Yoram Bachrach; Richard Everett 001; Edward Hughes 001; Angeliki Lazaridou; Joel Z. Leibo; Marc Lanctot; Michael Johanson; Wojciech M. Czarnecki; Thore Graepel (Artif. Intell., 2020)

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. - Raphael Köster; Kevin R. McKee; Richard Everett 001; Laura Weidinger; William S. Isaac; Edward Hughes 001; Edgar A. Duéñez-Guzmán; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2020)

Open Problems in Cooperative AI. - Allan Dafoe; Edward Hughes 001; Yoram Bachrach; Tantum Collins; Kevin R. McKee; Joel Z. Leibo; Kate Larson; Thore Graepel (CoRR, 2020)

Mastering Atari, Go, chess and shogi by planning with a learned model. - Julian Schrittwieser; Ioannis Antonoglou; Thomas Hubert; Karen Simonyan; Laurent Sifre; Simon Schmitt; Arthur Guez; Edward Lockhart; Demis Hassabis; Thore Graepel; Timothy P. Lillicrap; David Silver (Nat., 2020)

EigenGame: PCA as a Nash Equilibrium. - Ian Gemp; Brian McWilliams; Claire Vernade; Thore Graepel (ICLR, 2021)

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. - Joel Z. Leibo; Edgar A. Duéñez-Guzmán; Alexander Vezhnevets; John P. Agapiou; Peter Sunehag; Raphael Koster; Jayd Matyas; Charlie Beattie; Igor Mordatch; Thore Graepel (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)

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 (IJCAI, 2021)

Deep reinforcement learning models the emergent dynamics of human cooperation. - Kevin R. McKee; Edward Hughes 001; Tina O. Zhu; Martin J. Chadwick; Raphael Koster; Antonio García Castañeda; Charlie Beattie; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2021)

A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! - Thore Graepel; Ralf Herbrich (CoRR, 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

EigenGame Unloaded: When playing games is better than optimizing. - Ian Gemp; Brian McWilliams; Claire Vernade; Thore Graepel (ICLR, 2022)

NeuPL: Neural Population Learning. - Siqi Liu 002; Luke Marris; Daniel Hennes; Josh Merel; Nicolas Heess; Thore Graepel (ICLR, 2022)

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria. - Kavya Kopparapu; Edgar A. Duéñez-Guzmán; Jayd Matyas; Alexander Sasha Vezhnevets; John P. Agapiou; Kevin R. McKee; Richard Everett 001; Janusz Marecki; Joel Z. Leibo; Thore Graepel (CoRR, 2022)

Game Theoretic Rating in N-player general-sum games with Equilibria. - Luke Marris; Marc Lanctot; Ian Gemp; Shayegan Omidshafiei; Stephen McAleer; Jerome T. Connor; Karl Tuyls; Thore Graepel (CoRR, 2022)

From motor control to team play in simulated humanoid football. - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech M. Czarnecki; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Sci. Robotics, 2022)

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech Czarnecki 001; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Zenodo, 2022)

PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis. - Yan Wu; Esther Wershof; Sebastian M. Schmon; Marcel Nassar; Blazej Osinski; Ridvan Eksi; Kun Zhang; Thore Graepel (CoRR, 2024)

Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game. - Edward Hughes 001; Tina O. Zhu; Martin J. Chadwick; Raphael Koster; Antonio García Castañeda; Charles Beattie; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2025)