Thore Graepel

发表

Invariant Pattern Recognition by Semi-Definite Programming Machines

Thore Graepel, Ralf Herbrich, T. Graepel, 2003, NIPS.

Collaborative learning of preference rankings

Tim Salimans, Thore Graepel, Ulrich Paquet, 2012, RecSys.

PAC-Bayesian pattern classification with kernels: theory, algorithms, and an application to the game of Go

Thore Graepel, T. Graepel, 2002 .

Generalization Bounds for the Area Under the ROC Curve

Dan Roth, Sariel Har-Peled, Shivani Agarwal, 2005, J. Mach. Learn. Res..

Automated feature generation from structured knowledge

Gjergji Kasneci, Thore Graepel, Ralf Herbrich, 2011, CIKM '11.

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Demis Hassabis, Karen Simonyan, David Silver, 2018, Science.

Novel tools to streamline the conference review process: experiences from SIGKDD'09

Peter A. Flach, Mohammed J. Zaki, Thore Graepel, 2010, SKDD.

How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing

Tom Minka, Thore Graepel, Yoram Bachrach, 2012, ICML.

Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations

Thore Graepel, T. Graepel, 2003, ICML.

Negotiating team formation using deep reinforcement learning

Joel Z. Leibo, Thore Graepel, Yoram Bachrach, 2020, Artif. Intell..

A model-learner pattern for bayesian reasoning

Claudio V. Russo, Andrew D. Gordon, Sriram K. Rajamani, 2013, POPL.

Large scale data analysis and modelling in online services and advertising

Thore Graepel, Ralf Herbrich, 2008, KDD.

Learning Shared Representations in Multi-task Reinforcement Learning pdf

John Shawe-Taylor, Thore Graepel, Diana Borsa, 2016, ArXiv.

Re-evaluating evaluation

Thore Graepel, Karl Tuyls, David Balduzzi, 2018, NeurIPS.

Rip-off: playing the cooperative negotiation game

Pushmeet Kohli, Thore Graepel, Yoram Bachrach, 2011, AAMAS.

Matchbox: Large Scale Bayesian Recommendations

Thore Graepel, Ralf Herbrich, David Stern, 2009 .

Bayesian Transduction

Klaus Obermayer, Thore Graepel, Ralf Herbrich, 1999, NIPS.

Modelling Uncertainty in the Game of Go

Thore Graepel, David H. Stern, David J. C. MacKay, 2004, NIPS.

DBrev: Dreaming of a Database Revolution

Gjergji Kasneci, Thore Graepel, Jurgen Van Gael, 2011, CIDR.

Bayesian Knowledge Corroboration with Logical Rules and User Feedback

Gjergji Kasneci, Thore Graepel, Ralf Herbrich, 2010, ECML/PKDD.

SIGMa: simple greedy matching for aligning large knowledge bases

Gjergji Kasneci, Zoubin Ghahramani, Thore Graepel, 2012, KDD.

An Annealed Self-Organizing Map for Source Channel Coding

Klaus Obermayer, Thore Graepel, Matthias Burger, 1997, NIPS.

Sparsity vs. Large Margins for Linear Classifiers: A Small Sample Size Study

Thore Graepel, Ralf Herbrich, John Shawe-Taylor, 2000 .

Machine learning and games

Johannes Fürnkranz, Michael H. Bowling, Thore Graepel, 2006, Machine Learning.

Bayesian pattern ranking for move prediction in the game of Go

Thore Graepel, Ralf Herbrich, David H. Stern, 2006, ICML.

A Stochastic Self-Organizing Map for Proximity Data

Klaus Obermayer, Thore Graepel, K. Obermayer, 1999, Neural Computation.

Neural Networks in Economics

Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, 1999 .

Phase Transitions in Soft Topographic Vector Quantization

Klaus Obermayer, Thore Graepel, Matthias Burger, 1997, ICANN.

Symmetric Decomposition of Asymmetric Games

Shane Legg, Rahul Savani, Joel Z. Leibo, 2017, Scientific Reports.

Coherent Inference on Optimal Play in Game Trees

Thore Graepel, David H. Stern, Philipp Hennig, 2010, AISTATS.

Colonel Blotto on Facebook: the effect of social relations on strategic interaction

Pushmeet Kohli, Michael Kearns, Thore Graepel, 2012, WebSci '12.

Learning to Identify Historical Figures for Timeline Creation from Wikipedia Articles

Stephen Clark, Sandro Bauer, Thore Graepel, 2014, SocInfo Workshops.

Bounds and dynamics for empirical game theoretic analysis

Csaba Szepesvári, Joel Z. Leibo, Thore Graepel, 2019, Autonomous Agents and Multi-Agent Systems.

Adaptive Mechanism Design: Learning to Promote Cooperation

John Shawe-Taylor, Thore Graepel, Tobias Baumann, 2018, 2020 International Joint Conference on Neural Networks (IJCNN).

Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

Claudio V. Russo, Sriram K. Rajamani, Thore Graepel, 2013, POPL 2015.

Collaborative Learning of Preference Relations

Tim Salimans, Thore Graepel, Ulrich Paquet, 2012 .

Inferring the Demographics of Search Users

Milad Shokouhi, Thore Graepel, Bin Bi Ucla, 2013 .

Knowing what we don't know in NCAA Football ratings: Understanding and using structured uncertainty

Thore Graepel, Daniel Tarlow, Thomas P. Minka, 2014 .

A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work

Thore Graepel, Ralf Herbrich, T. Graepel, 2000, NIPS.

Special Issue on Machine Learning and Games

Johannes Fürnkranz, Michael H. Bowling, Michael Bowling, 2006 .

A Large Deviation Bound for the Area Under the ROC Curve

Dan Roth, Shivani Agarwal, Thore Graepel, 2004, NIPS.

A Generalised Method for Empirical Game Theoretic Analysis

Joel Z. Leibo, Thore Graepel, Karl Tuyls, 2018, AAMAS.

TrueSkillTM: A Bayesian Skill Rating System

Tom Minka, Thore Graepel, Ralf Herbrich, 2006, NIPS.

Combining Conjugate Direction Methods with Stochastic Approximation of Gradients

Nicol N. Schraudolph, Thore Graepel, T. Graepel, 2003, AISTATS.

Inequity aversion improves cooperation in intertemporal social dilemmas

Joel Z. Leibo, Iain Dunning, Thore Graepel, 2018, NeurIPS.

Kernel Matrix Completion by Semidefinite Programming

Thore Graepel, T. Graepel, 2002, ICANN.

Mastering Atari, Go, chess and shogi by planning with a learned model

Demis Hassabis, Karen Simonyan, David Silver, 2019, Nature.

Sparsity vs. Large Margins for Linear Classifiers

John Shawe-Taylor, Thore Graepel, Ralf Herbrich, 2000, COLT.

Large Scale Bayes Point Machines

Thore Graepel, Ralf Herbrich, T. Graepel, 2000, NIPS.

Guest Editorial: Machine Learning and Games

Johannes Fürnkranz, Michael H. Bowling, Michael Bowling, 2006 .

Inequity aversion resolves intertemporal social dilemmas pdf

Joel Z. Leibo, Iain Dunning, Thore Graepel, 2018, ArXiv.

Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward pdf

Guy Lever, Joel Z. Leibo, Max Jaderberg, 2018, AAMAS.

ML Confidential: Machine Learning on Encrypted Data

Michael Naehrig, Thore Graepel, Kristin E. Lauter, 2012, ICISC.

Malthusian Reinforcement Learning

Joel Z. Leibo, Iain Dunning, Peter Sunehag, 2018, AAMAS.

Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming

John Shawe-Taylor, Thore Graepel, Jaz S. Kandola, 2003, COLT.

Structure from failure

Brendan Murphy, Thore Graepel, Ralf Herbrich, 2007 .

Inferring the demographics of search users: social data meets search queries

Milad Shokouhi, Thore Graepel, Michal Kosinski, 2013, WWW.

Support vector learning for ordinal regression

Klaus Obermayer, Thore Graepel, Ralf Herbrich, 1999 .

Automated Probabilistic Modelling for Relational Data

Thore Graepel, Sameer Singh, T. Graepel, 2013 .

WWW 2009 MADRID! Track: Data Mining / Session: Statistical Methods Matchbox: Large Scale Online Bayesian Recommendations

Thore Graepel, Ralf Herbrich, David H. Stern, 2022 .

NEURAL NETWORKS IN ECONOMICS Background , Applications and New Developments

Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, 2000 .

The Kernel Gibbs Sampler

Thore Graepel, Ralf Herbrich, T. Graepel, 2000, NIPS.

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems

Tom Eccles, Guy Lever, Joel Z. Leibo, 2019, Artificial Life Conference Proceedings.

Scalable clustering and keyword suggestion for online advertisements

Joaquin Quiñonero Candela, Anton Schwaighofer, Thore Graepel, 2009, KDD Workshop on Data Mining and Audience Intelligence for Advertising.

Crowd IQ: aggregating opinions to boost performance

Gjergji Kasneci, Thore Graepel, Yoram Bachrach, 2012, AAMAS.

From Margin to Sparsity

Thore Graepel, Ralf Herbrich, Robert C. Williamson, 2000, NIPS.

Emergent Coordination Through Competition pdf

Guy Lever, Siqi Liu, Thore Graepel, 2019, ICLR.

TrueSkill Through Time: Revisiting the History of Chess

Tom Minka, Thore Graepel, Ralf Herbrich, 2007, NIPS.

Multiagent Reinforcement Learning in Games with an Iterated Dominance Solution

Tor Lattimore, Satinder Singh, Thore Graepel, 2019 .

Your digital image: factors behind demographic and psychometric predictions from social network profiles

Pushmeet Kohli, Thore Graepel, Yoram Bachrach, 2014, AAMAS.

Compiling Relational Database Schemata into Probabilistic Graphical Models pdf

Thore Graepel, Sameer Singh, T. Graepel, 2012, ArXiv.

PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification

John Shawe-Taylor, Thore Graepel, Ralf Herbrich, 2005, Machine Learning.

Game Plan: What AI can do for Football, and What Football can do for AI pdf

Michal Valko, Romuald Elie, Andrew Jaegle, 2020, J. Artif. Intell. Res..

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm pdf

Demis Hassabis, Karen Simonyan, David Silver, 2017, ArXiv.

Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems

Nicol N. Schraudolph, Thore Graepel, 2002, ICANN.

Kernel Topic Models

Thore Graepel, Ralf Herbrich, David H. Stern, 2011, AISTATS.

The Wreath Process: A totally generative model of geometric shape based on nested symmetries pdf

Andrew D. Gordon, Thore Graepel, Diana Borsa, 2015, ArXiv.

Multi-agent Reinforcement Learning in Sequential Social Dilemmas pdf

Joel Z. Leibo, Thore Graepel, Marc Lanctot, 2017, AAMAS.

Classi cation on Pairwise Proximity

Klaus Obermayer, Thore Graepel, Ralf Herbrich, 2007 .

Crowd IQ: measuring the intelligence of crowdsourcing platforms

Gjergji Kasneci, Thore Graepel, Yoram Bachrach, 2012, WebSci '12.

Semi-Definite Programming by Perceptron Learning

John Shawe-Taylor, Thore Graepel, Ralf Herbrich, 2003, NIPS.

Open Problems in Cooperative AI pdf

Joel Z. Leibo, Thore Graepel, Kate Larson, 2020, ArXiv.

EigenGame: PCA as a Nash Equilibrium pdf

Thore Graepel, Ian Gemp, Brian McWilliams, 2020, ICLR.

Biases for Emergent Communication in Multi-agent Reinforcement Learning

Tom Eccles, Guy Lever, Thore Graepel, 2019, NeurIPS.

Manifestations of user personality in website choice and behaviour on online social networks

Pushmeet Kohli, Thore Graepel, Yoram Bachrach, 2013, Machine Learning.

Learning to solve game trees

Thore Graepel, Ralf Herbrich, David H. Stern, 2007, ICML '07.

Differentiable Game Mechanics

Thore Graepel, Karl Tuyls, Jakob N. Foerster, 2019, J. Mach. Learn. Res..

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

David Silver, Thore Graepel, Karl Tuyls, 2017, NIPS.

Regression Models for Ordinal Data: A Machine Learning Approach

Klaus Obermayer, Thore Graepel, Ralf Herbrich, 1999 .

Conjugate Directions for Stochastic Gradient Descent

Nicol N. Schraudolph, Thore Graepel, 2002, ICANN.

Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model

Scott Sanner, Sarvnaz Karimi, Thore Graepel, 2011, CIKM '11.

A Comparison of learning algorithms on the Arcade Learning Environment pdf

Thore Graepel, Aaron Defazio, T. Graepel, 2014, ArXiv.

Automatic Curricula in Deep Multi-Agent Reinforc ement Learning

Thore Graepel, 2020, AAMAS.

The Mechanics of n-Player Differentiable Games pdf

Thore Graepel, Karl Tuyls, Jakob N. Foerster, 2018, ICML.

A Neural Architecture for Designing Truthful and Efficient Auctions pdf

Thore Graepel, Yoram Bachrach, Andrea Tacchetti, 2019, ArXiv.

A multi-agent reinforcement learning model of common-pool resource appropriation

Joel Z. Leibo, Thore Graepel, Karl Tuyls, 2017, NIPS.

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures

Xinhua Zhang, Thore Graepel, Ralf Herbrich, 2010, AISTATS.

Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests

Thore Graepel, Yoram Bachrach, Peter B. Key, 2012, AAAI.

Collaborative Expert Portfolio Management

Luca Pulina, Armando Tacchella, Thore Graepel, 2010, AAAI.

Learning to Play No-Press Diplomacy with Best Response Policy Iteration pdf

Tom Eccles, Satinder Singh, Julien P'erolat, 2020, NeurIPS.

Gaussian Process Regression: Active Data Selection and Test Point Rejection

Klaus Obermayer, Thore Graepel, Sambu Seo, 2000, DAGM-Symposium.

Bayes Point Machines

Colin Campbell, Thore Graepel, Ralf Herbrich, 2001, J. Mach. Learn. Res..

Poisson-Networks: A Model for Structured Poisson Processes.

Thore Graepel, Ralf Herbrich, Shyamsundar Rajaram, 2005 .

CoBayes: bayesian knowledge corroboration with assessors of unknown areas of expertise

Gjergji Kasneci, Thore Graepel, David H. Stern, 2011, WSDM '11.

The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution

Guy Lever, Pushmeet Kohli, Chrisantha Fernando, 2019, AAMAS.

Probabilistic Programs as Spreadsheet Queries

Claudio V. Russo, Andrew D. Gordon, Thore Graepel, 2015, ESOP.

Sociable killers: understanding social relationships in an online first-person shooter game

Xiang Cao, Abigail Sellen, Thore Graepel, 2011, CSCW '11.

Large Margin Rank Boundaries for Ordinal Regression

Thore Graepel, Darren Gehring, T. Graepel, 2000 .

Automated probabilistic modeling for relational data

Thore Graepel, Sameer Singh, 2013, CIKM.

Ranking and Matchmaking

Thore Graepel, Ralf Herbrich, T. Graepel, 2006 .

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences pdf

Joel Z. Leibo, Thore Graepel, Matthew Botvinick, 2020, ArXiv.

Generalisation Error Bounds for Sparse Linear Classifiers

John Shawe-Taylor, Thore Graepel, Ralf Herbrich, 2000, COLT.

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Guy Lever, Joel Z. Leibo, Demis Hassabis, 2018, Science.

Open-ended Learning in Symmetric Zero-sum Games

Max Jaderberg, Wojciech Czarnecki, Thore Graepel, 2019, ICML.

Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine

Joaquin Quiñonero Candela, Thore Graepel, Ralf Herbrich, 2010, ICML.

Tabular: a schema-driven probabilistic programming language

Claudio V. Russo, Andrew D. Gordon, Thore Graepel, 2014, POPL.

Learning a Theory of Marriage (and Other Relations) from a Web Corpus

Stephen Clark, Sandro Bauer, Thore Graepel, 2014, ECIR.

Smooth markets: A basic mechanism for organizing gradient-based learners pdf

Joel Z. Leibo, Thore Graepel, Georgios Piliouras, 2020, ICLR.

Mastering the game of Go without human knowledge

Demis Hassabis, Karen Simonyan, Fan Hui, 2017, Nature.

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research pdf

Joel Z. Leibo, Thore Graepel, Marc Lanctot, 2019, ArXiv.

Robust Bayes Point Machines

Colin Campbell, Thore Graepel, Ralf Herbrich, 2000, ESANN.

Introduction to the Special Issue on Learning Theory

Thore Graepel, Ralf Herbrich, 2003, J. Mach. Learn. Res..

Mastering the game of Go with deep neural networks and tree search

Demis Hassabis, Koray Kavukcuoglu, David Silver, 2016, Nature.

Learning on Graphs in the Game of Go

Thore Graepel, Ralf Herbrich, Marco Krüger, 2001, ICANN.

Kernel Methods for Document Filtering

Claudio Gentile, John Shawe-Taylor, Jean-Michel Renders, 2002, TREC.

Score-Based Bayesian Skill Learning

Scott Sanner, Thore Graepel, Wray L. Buntine, 2012, ECML/PKDD.

Relational Forward Models for Multi-Agent Learning pdf

H. Francis Song, Thore Graepel, Pedro A. M. Mediano, 2018, ICLR.

PAC-Bayesian Pattern Classification with kernels

Thore Graepel, T. Graepel, 2002 .

LEARNING TO FIGHT

Thore Graepel, Ralf Herbrich, Julian Gold, 2004 .

Self-organizing maps: Generalizations and new optimization techniques

Klaus Obermayer, Thore Graepel, Matthias Burger, 1998, Neurocomputing.

A Generalized Training Approach for Multiagent Learning pdf

Guy Lever, Thore Graepel, Karl Tuyls, 2020, ICLR.

Personality and patterns of Facebook usage

Pushmeet Kohli, Thore Graepel, Yoram Bachrach, 2012, WebSci '12.

Gaussian process regression: active data selection and test point rejection

Klaus Obermayer, Thore Graepel, Sambu Seo, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

EigenGame Unloaded: When playing games is better than optimizing pdf

Brian McWilliams, Thore Graepel, Ian Gemp, 2021, ArXiv.

A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! pdf

Thore Graepel, Ralf Herbrich, 2021, ArXiv.

Deep reinforcement learning models the emergent dynamics of human cooperation pdf

Joel Z. Leibo, Martin J. Chadwick, Thore Graepel, 2021, ArXiv.

A Neural Network Auction For Group Decision Making Over a Continuous Space

Tom Eccles, Thore Graepel, Yoram Bachrach, 2021, IJCAI.

From Motor Control to Team Play in Simulated Humanoid Football pdf

Yuval Tassa, Guy Lever, Thore Graepel, 2021, Sci. Robotics.

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot pdf

Joel Z. Leibo, Peter Sunehag, Thore Graepel, 2021, ICML.

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria pdf

Joel Z. Leibo, Thore Graepel, Alexander Sasha Vezhnevets, 2022, ArXiv.

Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

Thore Graepel, Karl Tuyls, Marc Lanctot, 2021, ICML.

Behavioral Game Theory on Online Social Networks : Colonel Blotto is on Facebook

Pushmeet Kohli, Thore Graepel, Yoram Bachrach, 2011 .

1 The Structure of Version Space

Thore Graepel, Ralf Herbrich, Robert C. Williamson, 2002 .

Correspondence Analysis for Visualizing Interplay of Pitch Class, Key, and Composer

Klaus Obermayer, Benjamin Blankertz, Thore Graepel, 2003 .

Gaussian Process Regression: Active Data Selection and Test Point

Klaus Obermayer, Thore Graepel, Sambu Seo, 2000 .

Using unlabeled data for supervised learning

Klaus Obermayer, Thore Graepel, Ralf Herbrich, 1999 .

Comparing Feature-Based Models of Harmony

Thore Graepel, Martin Rohrmeier, T. Graepel, 2012 .