Thore Graepel - RankMe

157 papers A* 30A 23B 2C 6Misc 1Journal 66Unranked 27

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game. CoRR Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio García Castañeda, Charles Beattie, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo
2024	J	`jnl`	Crowd IQ - Aggregating Opinions to Boost Performance. CoRR Michal Kosinski, Yoram Bachrach, Thore Graepel, Gjergji Kasneci, Jurgen Van Gael
2024	J	`jnl`	PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis. CoRR Yan Wu, Esther Wershof, Sebastian M. Schmon, Marcel Nassar, Blazej Osinski, Ridvan Eksi, Kun Zhang, Thore Graepel
2022	A*	`conf`	EigenGame Unloaded: When playing games is better than optimizing. ICLR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2022	J	`jnl`	From motor control to team play in simulated humanoid football. Sci. Robotics Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess
2022	J	`jnl`	Game Theoretic Rating in N-player general-sum games with Equilibria. CoRR Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen McAleer, Jerome T. Connor, Karl Tuyls, Thore Graepel
2022	J	`jnl`	Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria. CoRR Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas, Alexander Sasha Vezhnevets, John P. Agapiou, Kevin R. McKee, Richard Everett, Janusz Marecki, Joel Z. Leibo, Thore Graepel
2022	A*	`conf`	NeuPL: Neural Population Learning. ICLR Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel
2022	J	`jnl`	NeuPL: Neural Population Learning. CoRR Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel
2021	A*	`conf`	A Neural Network Auction For Group Decision Making Over a Continuous Space. IJCAI Yoram Bachrach, Ian Gemp, Marta Garnelo, János Kramár, Tom Eccles, Dan Rosenbaum, Thore Graepel
2021	J	`jnl`	A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! CoRR Thore Graepel, Ralf Herbrich
2021	J	`jnl`	Deep reinforcement learning models the emergent dynamics of human cooperation. CoRR Kevin R. McKee, Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio García Castañeda, Charlie Beattie, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo
2021	J	`jnl`	EigenGame Unloaded: When playing games is better than optimizing. CoRR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2021	A*	`conf`	EigenGame: PCA as a Nash Equilibrium. ICLR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2021	J	`jnl`	From Motor Control to Team Play in Simulated Humanoid Football. CoRR Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess
2021	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 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
2021	A*	`conf`	Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers. ICML Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
2021	J	`jnl`	Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers. CoRR Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
2021	A*	`conf`	Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. ICML Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel
2021	J	`jnl`	Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. CoRR Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel
2020	A*	`conf`	A Generalized Training Approach for Multiagent Learning. ICLR Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rémi Munos
2020	B	`conf`	Adaptive Mechanism Design: Learning to Promote Cooperation. IJCNN Tobias Baumann, Thore Graepel, John Shawe-Taylor
2020	A	`conf`	Automatic Curricula in Deep Multi-Agent Reinforc ement Learning. AAMAS Thore Graepel
2020	J	`jnl`	Bounds and dynamics for empirical game theoretic analysis. Auton. Agents Multi Agent Syst. Karl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel
2020	J	`jnl`	EigenGame: PCA as a Nash Equilibrium. CoRR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2020	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. CoRR 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
2020	A*	`conf`	Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
2020	J	`jnl`	Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
2020	J	`jnl`	Mastering Atari, Go, chess and shogi by planning with a learned model. Nat. 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
2020	J	`jnl`	Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. CoRR Raphael Köster, Kevin R. McKee, Richard Everett, Laura Weidinger, William S. Isaac, Edward Hughes, Edgar A. Duéñez-Guzmán, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo
2020	J	`jnl`	Negotiating Team Formation Using Deep Reinforcement Learning. CoRR Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel
2020	J	`jnl`	Negotiating team formation using deep reinforcement learning. Artif. Intell. Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel
2020	J	`jnl`	Open Problems in Cooperative AI. CoRR Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel
2020	A*	`conf`	Smooth markets: A basic mechanism for organizing gradient-based learners. ICLR David Balduzzi, Wojciech M. Czarnecki, Tom Anthony, Ian Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel
2020	J	`jnl`	Smooth markets: A basic mechanism for organizing gradient-based learners. CoRR David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M. Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel
2020	—	`ch.`	Tabular: Probabilistic Inference from the Spreadsheet. Foundations of Probabilistic Programming Andrew D. Gordon, Claudio V. Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel, Daniel Tarlow
2019	J	`jnl`	A Generalized Training Approach for Multiagent Learning. CoRR Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rémi Munos
2019	J	`jnl`	A Neural Architecture for Designing Truthful and Efficient Auctions. CoRR Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach
2019	J	`jnl`	Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research. CoRR Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel
2019	A*	`conf`	Biases for Emergent Communication in Multi-agent Reinforcement Learning. NeurIPS Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel
2019	J	`jnl`	Biases for Emergent Communication in Multi-agent Reinforcement Learning. CoRR Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel
2019	J	`jnl`	Differentiable Game Mechanics. CoRR Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel
2019	J	`jnl`	Differentiable Game Mechanics. J. Mach. Learn. Res. Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel
2019	—	`conf`	Emergent Coordination Through Competition. ICLR (Poster) Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel
2019	J	`jnl`	Emergent Coordination Through Competition. CoRR Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel
2019	A	`conf`	Malthusian Reinforcement Learning. AAMAS Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel
2019	J	`jnl`	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. CoRR 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
2019	J	`jnl`	Open-ended Learning in Symmetric Zero-sum Games. CoRR David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Pérolat, Max Jaderberg, Thore Graepel
2019	A*	`conf`	Open-ended learning in symmetric zero-sum games. ICML David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Pérolat, Max Jaderberg, Thore Graepel
2019	C	`conf`	Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems. ALIFE Peter Sunehag, Guy Lever, Siqi Liu, Josh Merel, Nicolas Heess, Joel Z. Leibo, Edward Hughes, Tom Eccles, Thore Graepel
2019	—	`conf`	Relational Forward Models for Multi-Agent Learning. ICLR (Poster) 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
2019	A	`conf`	The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution. AAMAS Dylan Banarse, Yoram Bachrach, Siqi Liu, Guy Lever, Nicolas Heess, Chrisantha Fernando, Pushmeet Kohli, Thore Graepel
2018	A	`conf`	A Generalised Method for Empirical Game Theoretic Analysis. AAMAS Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel
2018	J	`jnl`	A Generalised Method for Empirical Game Theoretic Analysis. CoRR Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel
2018	J	`jnl`	Adaptive Mechanism Design: Learning to Promote Cooperation. CoRR Tobias Baumann, Thore Graepel, John Shawe-Taylor
2018	J	`jnl`	Human-level performance in first-person multiplayer games with population-based deep reinforcement learning. CoRR 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, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel
2018	A*	`conf`	Inequity aversion improves cooperation in intertemporal social dilemmas. NeurIPS Edward Hughes, 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
2018	J	`jnl`	Inequity aversion resolves intertemporal social dilemmas. CoRR Edward Hughes, 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
2018	J	`jnl`	Malthusian Reinforcement Learning. CoRR Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel
2018	A*	`conf`	Re-evaluating evaluation. NeurIPS David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel
2018	J	`jnl`	Re-evaluating evaluation. CoRR David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel
2018	J	`jnl`	Relational Forward Models for Multi-Agent Learning. CoRR Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia
2018	A*	`conf`	The Mechanics of n-Player Differentiable Games. ICML David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel
2018	J	`jnl`	The Mechanics of n-Player Differentiable Games. CoRR David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel
2018	A	`conf`	Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. AAMAS 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
2017	—	`conf`	A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. NIPS Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel
2017	J	`jnl`	A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. CoRR Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel
2017	—	`conf`	A multi-agent reinforcement learning model of common-pool resource appropriation. NIPS Julien Pérolat, Joel Z. Leibo, Vinícius Flores Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel
2017	J	`jnl`	A multi-agent reinforcement learning model of common-pool resource appropriation. CoRR Julien Pérolat, Joel Z. Leibo, Vinícius Flores Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel
2017	J	`jnl`	Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. CoRR 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, Demis Hassabis
2017	J	`jnl`	Mastering the game of Go without human knowledge. Nat. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
2017	A	`conf`	Multi-agent Reinforcement Learning in Sequential Social Dilemmas. AAMAS Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel
2017	J	`jnl`	Multi-agent Reinforcement Learning in Sequential Social Dilemmas. CoRR Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel
2017	J	`jnl`	Symmetric Decomposition of Asymmetric Games. CoRR Karl Tuyls, Julien Pérolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg
2017	J	`jnl`	Value-Decomposition Networks For Cooperative Multi-Agent Learning. CoRR 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
2016	J	`jnl`	Learning Shared Representations in Multi-task Reinforcement Learning. CoRR Diana Borsa, Thore Graepel, John Shawe-Taylor
2016	J	`jnl`	Mastering the game of Go with deep neural networks and tree search. Nat. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis
2015	A	`conf`	Probabilistic Programs as Spreadsheet Queries. ESOP Andrew D. Gordon, Claudio V. Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel, Daniel Tarlow
2015	J	`jnl`	The Wreath Process: A totally generative model of geometric shape based on nested symmetries. CoRR Diana Borsa, Thore Graepel, Andrew D. Gordon
2014	J	`jnl`	A Comparison of learning algorithms on the Arcade Learning Environment. CoRR Aaron Defazio, Thore Graepel
2014	A	`conf`	Learning a Theory of Marriage (and Other Relations) from a Web Corpus. ECIR Sandro Bauer, Stephen Clark, Laura Rimell, Thore Graepel
2014	—	`conf`	Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles. SocInfo Workshops Sandro Bauer, Stephen Clark, Thore Graepel
2014	J	`jnl`	Manifestations of user personality in website choice and behaviour on online social networks. Mach. Learn. Michal Kosinski, Yoram Bachrach, Pushmeet Kohli, David Stillwell, Thore Graepel
2014	A*	`conf`	Tabular: a schema-driven probabilistic programming language. POPL Andrew D. Gordon, Thore Graepel, Nicolas Rolland, Claudio V. Russo, Johannes Borgström, John Guiver
2014	A	`conf`	Your digital image: factors behind demographic and psychometric predictions from social network profiles. AAMAS Yoram Bachrach, Thore Graepel, Pushmeet Kohli, Michal Kosinski, David Stillwell
2013	A*	`conf`	A model-learner pattern for bayesian reasoning. POPL Andrew D. Gordon, Mihhail Aizatulin, Johannes Borgström, Guillaume Claret, Thore Graepel, Aditya V. Nori, Sriram K. Rajamani, Claudio V. Russo
2013	A	`conf`	Automated probabilistic modeling for relational data. CIKM Sameer Singh, Thore Graepel
2013	A*	`conf`	Inferring the demographics of search users: social data meets search queries. WWW Bin Bi, Milad Shokouhi, Michal Kosinski, Thore Graepel
2013	A*	`conf`	SIGMa: simple greedy matching for aligning large knowledge bases. KDD Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani
2012	A	`conf`	Collaborative learning of preference rankings. RecSys Tim Salimans, Ulrich Paquet, Thore Graepel
2012	—	`conf`	Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel
2012	J	`jnl`	Compiling Relational Database Schemata into Probabilistic Graphical Models CoRR Sameer Singh, Thore Graepel
2012	A	`conf`	Crowd IQ: aggregating opinions to boost performance. AAMAS Yoram Bachrach, Thore Graepel, Gjergji Kasneci, Michal Kosinski, Jurgen Van Gael
2012	—	`conf`	Crowd IQ: measuring the intelligence of crowdsourcing platforms. WebSci Michal Kosinski, Yoram Bachrach, Gjergji Kasneci, Jurgen Van Gael, Thore Graepel
2012	A*	`conf`	How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing. ICML Yoram Bachrach, Thore Graepel, Tom Minka, John Guiver
2012	A	`conf`	Kernel Topic Models. AISTATS Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel
2012	Misc	`conf`	ML Confidential: Machine Learning on Encrypted Data. ICISC Thore Graepel, Kristin E. Lauter, Michael Naehrig
2012	J	`jnl`	ML Confidential: Machine Learning on Encrypted Data. IACR Cryptol. ePrint Arch. Thore Graepel, Kristin E. Lauter, Michael Naehrig
2012	—	`conf`	Personality and patterns of Facebook usage. WebSci Yoram Bachrach, Michal Kosinski, Thore Graepel, Pushmeet Kohli, David Stillwell
2012	A*	`conf`	Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests. AAAI Xi Alice Gao, Yoram Bachrach, Peter B. Key, Thore Graepel
2012	—	`conf`	Score-Based Bayesian Skill Learning. ECML/PKDD (1) Shengbo Guo, Scott Sanner, Thore Graepel, Wray L. Buntine
2012	J	`jnl`	SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases CoRR Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani
2011	A	`conf`	Automated feature generation from structured knowledge. CIKM Weiwei Cheng, Gjergji Kasneci, Thore Graepel, David H. Stern, Ralf Herbrich
2011	A	`conf`	CoBayes: bayesian knowledge corroboration with assessors of unknown areas of expertise. WSDM Gjergji Kasneci, Jurgen Van Gael, David H. Stern, Thore Graepel
2011	A	`conf`	DBrev: Dreaming of a Database Revolution. CIDR Gjergji Kasneci, Jurgen Van Gael, Thore Graepel
2011	A	`conf`	Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model. CIKM Scott Sanner, Shengbo Guo, Thore Graepel, Sadegh Kharazmi, Sarvnaz Karimi
2011	J	`jnl`	Kernel Topic Models CoRR Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel
2011	A	`conf`	Rip-off: playing the cooperative negotiation game. AAMAS Yoram Bachrach, Pushmeet Kohli, Thore Graepel
2011	A	`conf`	Sociable killers: understanding social relationships in an online first-person shooter game. CSCW Yan Xu, Xiang Cao, Abigail Sellen, Ralf Herbrich, Thore Graepel
2010	—	`conf`	Bayesian Knowledge Corroboration with Logical Rules and User Feedback. ECML/PKDD (2) Gjergji Kasneci, Jurgen Van Gael, Ralf Herbrich, Thore Graepel
2010	A	`conf`	Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. AISTATS Xinhua Zhang, Thore Graepel, Ralf Herbrich
2010	A	`conf`	Coherent Inference on Optimal Play in Game Trees. AISTATS Philipp Hennig, David H. Stern, Thore Graepel
2010	A*	`conf`	Collaborative Expert Portfolio Management. AAAI David H. Stern, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, Armando Tacchella
2010	A*	`conf`	Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. ICML Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich
2009	A*	`conf`	Matchbox: large scale online bayesian recommendations. WWW David H. Stern, Ralf Herbrich, Thore Graepel
2009	J	`jnl`	Novel tools to streamline the conference review process: experiences from SIGKDD'09. SIGKDD Explor. Peter A. Flach, Sebastian Spiegler, Bruno Golénia, Simon Price, John Guiver, Ralf Herbrich, Thore Graepel, Mohammed J. Zaki
2009	—	`conf`	Scalable clustering and keyword suggestion for online advertisements. KDD Workshop on Data Mining and Audience Intelligence for Advertising Anton Schwaighofer, Joaquin Quiñonero Candela, Thomas Borchert, Thore Graepel, Ralf Herbrich
2008	A*	`conf`	Large scale data analysis and modelling in online services and advertising. KDD Thore Graepel, Ralf Herbrich
2007	A*	`conf`	Learning to solve game trees. ICML David H. Stern, Ralf Herbrich, Thore Graepel
2007	—	`conf`	TrueSkill Through Time: Revisiting the History of Chess. NIPS Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel
2006	A*	`conf`	Bayesian pattern ranking for move prediction in the game of Go. ICML David H. Stern, Ralf Herbrich, Thore Graepel
2006	J	`jnl`	Machine learning and games. Mach. Learn. Michael H. Bowling, Johannes Fürnkranz, Thore Graepel, Ron Musick
2006	—	`conf`	TrueSkill NIPS Ralf Herbrich, Tom Minka, Thore Graepel
2005	J	`jnl`	Generalization Bounds for the Area Under the ROC Curve. J. Mach. Learn. Res. Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth
2005	J	`jnl`	PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. Mach. Learn. Thore Graepel, Ralf Herbrich, John Shawe-Taylor
2005	A	`conf`	Poisson-Networks: A Model for Structured Poisson Processes. AISTATS Shyamsundar Rajaram, Thore Graepel, Ralf Herbrich
2004	—	`conf`	A Large Deviation Bound for the Area Under the ROC Curve. NIPS Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth
2004	—	`conf`	Modelling Uncertainty in the Game of Go. NIPS David H. Stern, Thore Graepel, David J. C. MacKay
2003	A	`conf`	Combining Conjugate Direction Methods with Stochastic Approximation of Gradients. AISTATS Nicol N. Schraudolph, Thore Graepel
2003	J	`jnl`	Introduction to the Special Issue on Learning Theory. J. Mach. Learn. Res. Ralf Herbrich, Thore Graepel
2003	—	`conf`	Invariant Pattern Recognition by Semi-Definite Programming Machines. NIPS Thore Graepel, Ralf Herbrich
2003	A*	`conf`	Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming. COLT Jaz S. Kandola, Thore Graepel, John Shawe-Taylor
2003	—	`conf`	Semi-Definite Programming by Perceptron Learning. NIPS Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor
2003	A*	`conf`	Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations. ICML Thore Graepel
2002	J	`jnl`	A PAC-Bayesian margin bound for linear classifiers. IEEE Trans. Inf. Theory Ralf Herbrich, Thore Graepel
2002	C	`conf`	Conjugate Directions for Stochastic Gradient Descent. ICANN Nicol N. Schraudolph, Thore Graepel
2002	C	`conf`	Kernel Matrix Completion by Semidefinite Programming. ICANN Thore Graepel
2002	—	`conf`	Kernel Methods for Document Filtering. TREC Nicola Cancedda, Cyril Goutte, Jean-Michel Renders, Nicolò Cesa-Bianchi, Alex Conconi, Yaoyong Li, John Shawe-Taylor, Alexei Vinokourov, Thore Graepel, Claudio Gentile
2002	—	`—`	PAC-Bayesian pattern classification with kernels: theory, algorithms, and an application to the game of Go. Thore Graepel
2002	C	`conf`	Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems. ICANN Thore Graepel, Nicol N. Schraudolph
2001	J	`jnl`	Bayes Point Machines. J. Mach. Learn. Res. Ralf Herbrich, Thore Graepel, Colin Campbell
2001	C	`conf`	Learning on Graphs in the Game of Go. ICANN Thore Graepel, Mike Goutrié, Marco Krüger, Ralf Herbrich
2000	—	`conf`	A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. NIPS Ralf Herbrich, Thore Graepel
2000	—	`conf`	From Margin to Sparsity. NIPS Thore Graepel, Ralf Herbrich, Robert C. Williamson
2000	—	`conf`	Gaussian Process Regression: Active Data Selection and Test Point Rejection. DAGM-Symposium Sambu Seo, Marko Wallat, Thore Graepel, Klaus Obermayer
2000	—	`conf`	Gaussian Process Regression: Active Data Selection and Test Point Rejection. IJCNN (3) Sambu Seo, Marko Wallat, Thore Graepel, Klaus Obermayer
2000	A*	`conf`	Generalisation Error Bounds for Sparse Linear Classifiers. COLT Thore Graepel, Ralf Herbrich, John Shawe-Taylor
2000	—	`conf`	Large Scale Bayes Point Machines. NIPS Ralf Herbrich, Thore Graepel
2000	B	`conf`	Robust Bayes Point Machines. ESANN Ralf Herbrich, Thore Graepel, Colin Campbell
2000	A*	`conf`	Sparsity vs. Large Margins for Linear Classifiers. COLT Ralf Herbrich, Thore Graepel, John Shawe-Taylor
2000	—	`conf`	The Kernel Gibbs Sampler. NIPS Thore Graepel, Ralf Herbrich
1999	J	`jnl`	A Stochastic Self-Organizing Map for Proximity Data. Neural Comput. Thore Graepel, Klaus Obermayer
1999	—	`conf`	Bayesian Transduction. NIPS Thore Graepel, Ralf Herbrich, Klaus Obermayer
1998	—	`conf`	Classification on Pairwise Proximity Data. NIPS Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer
1998	J	`jnl`	Self-organizing maps: Generalizations and new optimization techniques. Neurocomputing Thore Graepel, Matthias Burger, Klaus Obermayer
1997	—	`conf`	An Annealed Self-Organizing Map for Source Channel Coding. NIPS Matthias Burger, Thore Graepel, Klaus Obermayer
1997	C	`conf`	Phase Transitions in Soft Topographic Vector Quantization. ICANN Matthias Burger, Thore Graepel, Klaus Obermayer