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ORCIDThore Graepel
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- affiliation: Microsoft Research
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2020 – today
- 2024
[i47]Yan Wu, Esther Wershof, Sebastian M. Schmon, Marcel Nassar, Blazej Osinski, Ridvan Eksi, Kun Zhang, Thore Graepel:
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis. CoRR abs/2408.10609 (2024)- [i46]Michal Kosinski, Yoram Bachrach, Thore Graepel, Gjergji Kasneci, Jurgen Van Gael:
Crowd IQ - Aggregating Opinions to Boost Performance. CoRR abs/2410.10004 (2024) - 2022
- [j18]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:
From motor control to team play in simulated humanoid football. Sci. Robotics 7(69) (2022) - [c89]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame Unloaded: When playing games is better than optimizing. ICLR 2022 - [c88]Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel:
NeuPL: Neural Population Learning. ICLR 2022 - [d1]Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech 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:
Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". Zenodo, 2022
- [i45]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:
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria. CoRR abs/2201.01816 (2022) - [i44]Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel:
NeuPL: Neural Population Learning. CoRR abs/2202.07415 (2022) - [i43]Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen McAleer, Jerome T. Connor, Karl Tuyls, Thore Graepel:
Game Theoretic Rating in N-player general-sum games with Equilibria. CoRR abs/2210.02205 (2022) - 2021
- [j17]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:
Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 71: 41-88 (2021) - [c87]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame: PCA as a Nash Equilibrium. ICLR 2021 - [c86]Joel Z. Leibo, Edgar A. Duéñez-Guzmán, Alexander Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charlie Beattie, Igor Mordatch, Thore Graepel:
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. ICML 2021: 6187-6199 - [c85]Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel:
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers. ICML 2021: 7480-7491 - [c84]Yoram Bachrach, Ian Gemp, Marta Garnelo, János Kramár, Tom Eccles, Dan Rosenbaum, Thore Graepel:
A Neural Network Auction For Group Decision Making Over a Continuous Space. IJCAI 2021: 4976-4979 - [i42]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame Unloaded: When playing games is better than optimizing. CoRR abs/2102.04152 (2021) - [i41]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:
Deep reinforcement learning models the emergent dynamics of human cooperation. CoRR abs/2103.04982 (2021) - [i40]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:
From Motor Control to Team Play in Simulated Humanoid Football. CoRR abs/2105.12196 (2021) - [i39]Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel:
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers. CoRR abs/2106.09435 (2021) - [i38]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:
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. CoRR abs/2107.06857 (2021) - [i37]Thore Graepel, Ralf Herbrich:
A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! CoRR abs/2109.13889 (2021) - 2020
- [j16]Karl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel:
Bounds and dynamics for empirical game theoretic analysis. Auton. Agents Multi Agent Syst. 34(1): 7 (2020) - [j15]Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel:
Negotiating team formation using deep reinforcement learning. Artif. Intell. 288: 103356 (2020) - [j14]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:
Mastering Atari, Go, chess and shogi by planning with a learned model. Nat. 588(7839): 604-609 (2020) - [c83]Thore Graepel:
Automatic Curricula in Deep Multi-Agent Reinforc ement Learning. AAMAS 2020: 2 - [c82]David Balduzzi, Wojciech M. Czarnecki, Tom Anthony, Ian Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel:
Smooth markets: A basic mechanism for organizing gradient-based learners. ICLR 2020 - [c81]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:
A Generalized Training Approach for Multiagent Learning. ICLR 2020 - [c80]Tobias Baumann, Thore Graepel, John Shawe-Taylor:
Adaptive Mechanism Design: Learning to Promote Cooperation. IJCNN 2020: 1-7 - [c79]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:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS 2020 - [i36]David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M. Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel:
Smooth markets: A basic mechanism for organizing gradient-based learners. CoRR abs/2001.04678 (2020) - [i35]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:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR abs/2006.04635 (2020) - [i34]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame: PCA as a Nash Equilibrium. CoRR abs/2010.00554 (2020) - [i33]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:
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. CoRR abs/2010.09054 (2020) - [i32]Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel:
Negotiating Team Formation Using Deep Reinforcement Learning. CoRR abs/2010.10380 (2020) - [i31]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:
Game Plan: What AI can do for Football, and What Football can do for AI. CoRR abs/2011.09192 (2020) - [i30]Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel:
Open Problems in Cooperative AI. CoRR abs/2012.08630 (2020)
2010 – 2019
- 2019
- [j13]Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
Differentiable Game Mechanics. J. Mach. Learn. Res. 20: 84:1-84:40 (2019) - [c78]Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel:
Malthusian Reinforcement Learning. AAMAS 2019: 1099-1107 - [c77]Dylan Banarse, Yoram Bachrach, Siqi Liu, Guy Lever, Nicolas Heess, Chrisantha Fernando, Pushmeet Kohli, Thore Graepel:
The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution. AAMAS 2019: 1134-1142 - [c76]Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel:
Emergent Coordination Through Competition. ICLR (Poster) 2019 - [c75]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:
Relational Forward Models for Multi-Agent Learning. ICLR (Poster) 2019 - [c74]David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Pérolat, Max Jaderberg, Thore Graepel:
Open-ended learning in symmetric zero-sum games. ICML 2019: 434-443 - [c73]Peter Sunehag, Guy Lever, Siqi Liu, Josh Merel, Nicolas Heess, Joel Z. Leibo, Edward Hughes, Tom Eccles, Thore Graepel:
Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems. ALIFE 2019: 103-110 - [c72]Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel:
Biases for Emergent Communication in Multi-agent Reinforcement Learning. NeurIPS 2019: 13111-13121 - [i29]David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Pérolat, Max Jaderberg, Thore Graepel:
Open-ended Learning in Symmetric Zero-sum Games. CoRR abs/1901.08106 (2019) - [i28]Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel:
Emergent Coordination Through Competition. CoRR abs/1902.07151 (2019) - [i27]Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel:
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research. CoRR abs/1903.00742 (2019) - [i26]Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
Differentiable Game Mechanics. CoRR abs/1905.04926 (2019) - [i25]Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach:
A Neural Architecture for Designing Truthful and Efficient Auctions. CoRR abs/1907.05181 (2019) - [i24]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:
A Generalized Training Approach for Multiagent Learning. CoRR abs/1909.12823 (2019) - [i23]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:
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. CoRR abs/1911.08265 (2019) - [i22]Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel:
Biases for Emergent Communication in Multi-agent Reinforcement Learning. CoRR abs/1912.05676 (2019) - 2018
- [c71]Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel:
A Generalised Method for Empirical Game Theoretic Analysis. AAMAS 2018: 77-85 - [c70]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:
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. AAMAS 2018: 2085-2087 - [c69]David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
The Mechanics of n-Player Differentiable Games. ICML 2018: 363-372 - [c68]David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel:
Re-evaluating evaluation. NeurIPS 2018: 3272-3283 - [c67]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:
Inequity aversion improves cooperation in intertemporal social dilemmas. NeurIPS 2018: 3330-3340 - [i21]David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
The Mechanics of n-Player Differentiable Games. CoRR abs/1802.05642 (2018) - [i20]Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel:
A Generalised Method for Empirical Game Theoretic Analysis. CoRR abs/1803.06376 (2018) - [i19]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:
Inequity aversion resolves intertemporal social dilemmas. CoRR abs/1803.08884 (2018) - [i18]David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel:
Re-evaluating evaluation. CoRR abs/1806.02643 (2018) - [i17]Tobias Baumann, Thore Graepel, John Shawe-Taylor:
Adaptive Mechanism Design: Learning to Promote Cooperation. CoRR abs/1806.04067 (2018) - [i16]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:
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning. CoRR abs/1807.01281 (2018) - [i15]Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia:
Relational Forward Models for Multi-Agent Learning. CoRR abs/1809.11044 (2018) - [i14]Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel:
Malthusian Reinforcement Learning. CoRR abs/1812.07019 (2018) - 2017
- [j12]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:
Mastering the game of Go without human knowledge. Nat. 550(7676): 354-359 (2017) - [c66]Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel:
Multi-agent Reinforcement Learning in Sequential Social Dilemmas. AAMAS 2017: 464-473 - [c65]Julien Pérolat, Joel Z. Leibo, Vinícius Flores Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel:
A multi-agent reinforcement learning model of common-pool resource appropriation. NIPS 2017: 3643-3652 - [c64]Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel:
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. NIPS 2017: 4190-4203 - [i13]Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel:
Multi-agent Reinforcement Learning in Sequential Social Dilemmas. CoRR abs/1702.03037 (2017) - [i12]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:
Value-Decomposition Networks For Cooperative Multi-Agent Learning. CoRR abs/1706.05296 (2017) - [i11]Julien Pérolat, Joel Z. Leibo, Vinícius Flores Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel:
A multi-agent reinforcement learning model of common-pool resource appropriation. CoRR abs/1707.06600 (2017) - [i10]Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel:
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. CoRR abs/1711.00832 (2017) - [i9]Karl Tuyls, Julien Pérolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg:
Symmetric Decomposition of Asymmetric Games. CoRR abs/1711.05074 (2017) - [i8]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:
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. CoRR abs/1712.01815 (2017) - 2016
- [j11]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:
Mastering the game of Go with deep neural networks and tree search. Nat. 529(7587): 484-489 (2016) - [i7]Diana Borsa, Thore Graepel, John Shawe-Taylor:
Learning Shared Representations in Multi-task Reinforcement Learning. CoRR abs/1603.02041 (2016) - 2015
- [c63]Andrew D. Gordon, Claudio V. Russo, Marcin Szymczak, Johannes Borgström, Nicolas Rolland, Thore Graepel, Daniel Tarlow:
Probabilistic Programs as Spreadsheet Queries. ESOP 2015: 1-25 - [i6]Diana Borsa, Thore Graepel, Andrew D. Gordon:
The Wreath Process: A totally generative model of geometric shape based on nested symmetries. CoRR abs/1506.03041 (2015) - 2014
- [j10]Michal Kosinski, Yoram Bachrach, Pushmeet Kohli, David Stillwell, Thore Graepel:
Manifestations of user personality in website choice and behaviour on online social networks. Mach. Learn. 95(3): 357-380 (2014) - [c62]Yoram Bachrach, Thore Graepel, Pushmeet Kohli, Michal Kosinski, David Stillwell:
Your digital image: factors behind demographic and psychometric predictions from social network profiles. AAMAS 2014: 1649-1650 - [c61]Sandro Bauer, Stephen Clark, Laura Rimell, Thore Graepel:
Learning a Theory of Marriage (and Other Relations) from a Web Corpus. ECIR 2014: 591-597 - [c60]Andrew D. Gordon, Thore Graepel, Nicolas Rolland, Claudio V. Russo, Johannes Borgström, John Guiver:
Tabular: a schema-driven probabilistic programming language. POPL 2014: 321-334 - [c59]Sandro Bauer, Stephen Clark, Thore Graepel:
Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles. SocInfo Workshops 2014: 234-243 - [i5]Aaron Defazio, Thore Graepel:
A Comparison of learning algorithms on the Arcade Learning Environment. CoRR abs/1410.8620 (2014) - 2013
- [c58]Sameer Singh, Thore Graepel:
Automated probabilistic modeling for relational data. CIKM 2013: 1497-1500 - [c57]Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani:
SIGMa: simple greedy matching for aligning large knowledge bases. KDD 2013: 572-580 - [c56]Andrew D. Gordon, Mihhail Aizatulin, Johannes Borgström, Guillaume Claret, Thore Graepel, Aditya V. Nori, Sriram K. Rajamani, Claudio V. Russo:
A model-learner pattern for bayesian reasoning. POPL 2013: 403-416 - [c55]Bin Bi, Milad Shokouhi, Michal Kosinski, Thore Graepel:
Inferring the demographics of search users: social data meets search queries. WWW 2013: 131-140 - 2012
- [c54]Xi Alice Gao, Yoram Bachrach, Peter B. Key, Thore Graepel:
Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests. AAAI 2012: 38-44 - [c53]Yoram Bachrach, Thore Graepel, Gjergji Kasneci, Michal Kosinski, Jurgen Van Gael:
Crowd IQ: aggregating opinions to boost performance. AAMAS 2012: 535-542 - [c52]Thore Graepel, Kristin E. Lauter, Michael Naehrig:
ML Confidential: Machine Learning on Encrypted Data. ICISC 2012: 1-21 - [c51]Yoram Bachrach, Thore Graepel, Tom Minka, John Guiver:
How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing. ICML 2012 - [c50]Shengbo Guo, Scott Sanner, Thore Graepel, Wray L. Buntine:
Score-Based Bayesian Skill Learning. ECML/PKDD (1) 2012: 106-121 - [c49]Tim Salimans, Ulrich Paquet, Thore Graepel:
Collaborative learning of preference rankings. RecSys 2012: 261-264 - [c48]Yoram Bachrach, Michal Kosinski, Thore Graepel, Pushmeet Kohli, David Stillwell:
Personality and patterns of Facebook usage. WebSci 2012: 24-32 - [c47]Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel:
Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci 2012: 141-150 - [c46]Michal Kosinski, Yoram Bachrach, Gjergji Kasneci, Jurgen Van Gael, Thore Graepel:
Crowd IQ: measuring the intelligence of crowdsourcing platforms. WebSci 2012: 151-160 - [c45]Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel:
Kernel Topic Models. AISTATS 2012: 511-519 - [i4]Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani:
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases. CoRR abs/1207.4525 (2012) - [i3]Sameer Singh, Thore Graepel:
Compiling Relational Database Schemata into Probabilistic Graphical Models. CoRR abs/1212.0967 (2012) - [i2]Thore Graepel, Kristin E. Lauter, Michael Naehrig:
ML Confidential: Machine Learning on Encrypted Data. IACR Cryptol. ePrint Arch. 2012: 323 (2012) - 2011
- [c44]Yoram Bachrach, Pushmeet Kohli, Thore Graepel:
Rip-off: playing the cooperative negotiation game. AAMAS 2011: 1179-1180 - [c43]Gjergji Kasneci, Jurgen Van Gael, Thore Graepel:
DBrev: Dreaming of a Database Revolution. CIDR 2011: 191-194 - [c42]Weiwei Cheng, Gjergji Kasneci, Thore Graepel, David H. Stern, Ralf Herbrich:
Automated feature generation from structured knowledge. CIKM 2011: 1395-1404 - [c41]Scott Sanner, Shengbo Guo, Thore Graepel, Sadegh Kharazmi, Sarvnaz Karimi:
Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model. CIKM 2011: 1977-1980 - [c40]Yan Xu, Xiang Cao, Abigail Sellen, Ralf Herbrich, Thore Graepel:
Sociable killers: understanding social relationships in an online first-person shooter game. CSCW 2011: 197-206 - [c39]Gjergji Kasneci, Jurgen Van Gael, David H. Stern, Thore Graepel:
CoBayes: bayesian knowledge corroboration with assessors of unknown areas of expertise. WSDM 2011: 465-474 - [i1]Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel:
Kernel Topic Models. CoRR abs/1110.4713 (2011) - 2010
- [c38]David H. Stern, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, Armando Tacchella:
Collaborative Expert Portfolio Management. AAAI 2010: 179-184 - [c37]Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich:
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. ICML 2010: 13-20 - [c36]