World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
54646
World Ranking
2247
National Ranking
127

Overview

Thore Graepel is affiliated with Altos Labs in the United States and works primarily in the field of Computer Science with a focus on Artificial Intelligence. Their research spans several subfields, including Management Science and Operations Research, Safety Research, Economics and Econometrics, and Computer Vision and Pattern Recognition.

The scientist has authored numerous papers, with significant contributions appearing in venues such as arXiv (Cornell University), Zenodo (CERN European Organization for Nuclear Research), Nature, Science Robotics, and Artificial Intelligence. Key recent papers include:

  • Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver, 2023, arXiv (Cornell University)
  • Cooperative AI: machines must learn to find common ground, 2021, Nature
  • From motor control to team play in simulated humanoid football, 2022, Science Robotics
  • Crowd IQ -- Aggregating Opinions to Boost Performance, 2024, arXiv (Cornell University)
  • Negotiating team formation using deep reinforcement learning, 2020, Artificial Intelligence

The recurring topics in Thore Graepel's research address areas such as Reinforcement Learning in Robotics, Experimental Behavioral Economics Studies, Game Theory and Applications, Multi-Agent Systems and Negotiation, Sports Analytics and Performance, Artificial Intelligence in Games, and Auction Theory and Applications.

  • Reinforcement Learning in Robotics
  • Experimental Behavioral Economics Studies
  • Game Theory and Applications
  • Multi-Agent Systems and Negotiation
  • Sports Analytics and Performance
  • Artificial Intelligence in Games
  • Auction Theory and Applications

Frequent collaborators of Thore Graepel include Joel Z. Leibo, Karl Tuyls, Wojciech Marian Czarnecki, Yoram Bachrach, and Luke Marris. These relationships reflect ongoing joint work, particularly in topics bridging AI, multi-agent interactions, and learning strategies.

  • Joel Z. Leibo
  • Karl Tuyls
  • Wojciech Marian Czarnecki
  • Yoram Bachrach
  • Luke Marris

The publication record is dominated by contributions to arXiv (Cornell University) with 19 papers, supplemented by work in Zenodo, Nature, Science Robotics, and Artificial Intelligence. The scientist's work integrates AI methodologies with behavioral and economic models to explore computational approaches to decision-making and cooperation.

Best Publications

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

    David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez

  • Mastering the game of Go without human knowledge

    David Silver;Julian Schrittwieser;Karen Simonyan;Ioannis Antonoglou

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

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Private traits and attributes are predictable from digital records of human behavior

    Michal Kosinski;David Stillwell;Thore Graepel

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

    Julian Schrittwieser;Ioannis Antonoglou;Thomas Hubert;Karen Simonyan

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

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • TrueSkill™: A Bayesian Skill Rating System

    Ralf Herbrich;Tom Minka;Thore Graepel

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

    Thore Graepel;Joaquin Q. Candela;Thomas Borchert;Ralf Herbrich

  • Support vector learning for ordinal regression

    R. Herbrich;T. Graepel;K. Obermayer

  • Personality and patterns of Facebook usage

    Yoram Bachrach;Michal Kosinski;Thore Graepel;Pushmeet Kohli

  • Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.

    Max Jaderberg;Wojciech M. Czarnecki;Iain Dunning;Luke Marris

  • ML confidential: machine learning on encrypted data

    Thore Graepel;Kristin Lauter;Michael Naehrig

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • Multi-agent Reinforcement Learning in Sequential Social Dilemmas

    Joel Z. Leibo;Vinicius Zambaldi;Marc Lanctot;Janusz Marecki

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

    Max Jaderberg;Wojciech M. Czarnecki;Iain Dunning;Luke Marris

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

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

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

    Michal Kosinski;Yoram Bachrach;Pushmeet Kohli;David Stillwell

  • Gaussian process regression: active data selection and test point rejection

    Sambu Seo;M. Wallat;T. Graepel;K. Obermayer

  • Matchbox: large scale online bayesian recommendations

    David H. Stern;Ralf Herbrich;Thore Graepel

  • A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

    Marc Lanctot;Vinícius Flores Zambaldi;Audrunas Gruslys;Angeliki Lazaridou

  • Large Margin Rank Boundaries for Ordinal Regression

    Darren Gehring;Thore Graepel

Frequent Co-Authors

Ralf Herbrich
Ralf Herbrich Hasso Plattner Institute
Yoram Bachrach
Yoram Bachrach DeepMind (United Kingdom)
Karl Tuyls
Karl Tuyls DeepMind (United Kingdom)
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Marc Lanctot
Marc Lanctot DeepMind (United Kingdom)
Klaus Obermayer
Klaus Obermayer Technical University of Berlin
John Shawe-Taylor
John Shawe-Taylor University College London
David Silver
David Silver DeepMind (United Kingdom)
Andrew D. Gordon
Andrew D. Gordon Microsoft (United States)
Demis Hassabis
Demis Hassabis Google (United States)

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