Subcouples of codimension one and interpolation of operators that almost agree. - Peter Sunehag (J. Approx. Theory, 2004)

Emerge and spread models and word burstiness. - Peter Sunehag (AISTATS, 2007)

Semi-Markov kMeans Clustering and Activity Recognition from Body-Worn Sensors. - Matthew W. Robards; Peter Sunehag (ICDM, 2009)

Variable Metric Stochastic Approximation Theory. - Peter Sunehag; Jochen Trumpf; S. V. N. Vishwanathan; Nicol N. Schraudolph (AISTATS, 2009)

Consistency of Feature Markov Processes. - Peter Sunehag; Marcus Hutter (ALT, 2010)

Wearable sensor activity analysis using semi-Markov models with a grammar. - Owen Thomas; Peter Sunehag; Gideon Dror; Sungrack Yun; Sungwoong Kim; Matthew W. Robards; Alexander J. Smola; Daniel Green; Philo Saunders (Pervasive Mob. Comput., 2010)

Axioms for Rational Reinforcement Learning. - Peter Sunehag; Marcus Hutter (ALT, 2011)

Principles of Solomonoff Induction and AIXI. - Peter Sunehag; Marcus Hutter (Algorithmic Probability and Friends, 2011)

(Non-)Equivalence of Universal Priors. - Ian Wood; Peter Sunehag; Marcus Hutter (Algorithmic Probability and Friends, 2011)

Feature Reinforcement Learning in Practice. - Phuong Minh Nguyen; Peter Sunehag; Marcus Hutter (EWRL, 2011)

Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control. - Matthew W. Robards; Peter Sunehag (EWRL, 2011)

Sparse Kernel-SARSA(λ) with an Eligibility Trace. - Matthew W. Robards; Peter Sunehag; Scott Sanner; Bhaskara Marthi (ECML/PKDD (3), 2011)

Context Tree Maximizing. - Phuong Minh Nguyen; Peter Sunehag; Marcus Hutter (AAAI, 2012)

Optimistic AIXI. - Peter Sunehag; Marcus Hutter (AGI, 2012)

On Ensemble Techniques for AIXI Approximation. - Joel Veness; Peter Sunehag; Marcus Hutter (AGI, 2012)

Optimistic Agents Are Asymptotically Optimal. - Peter Sunehag; Marcus Hutter (Australasian Conference on Artificial Intelligence, 2012)

Coding of Non-Stationary Sources as a Foundation for Detecting Change Points and Outliers in Binary Time-Series. - Peter Sunehag; Wen Shao; Marcus Hutter (AusDM, 2012)

Adaptive Context Tree Weighting. - Alexander O'Neill; Marcus Hutter; Wen Shao; Peter Sunehag (DCC, 2012)

Recursive channel selection techniques for brain computer interfaces. - Gareth Oliver; Peter Sunehag; Tom Gedeon (EMBC, 2012)

Asynchronous Brain Computer Interface using Hidden Semi-Markov Models. - Gareth Oliver; Peter Sunehag; Tom Gedeon (EMBC, 2012)

Feature Reinforcement Learning using Looping Suffix Trees. - Mayank Daswani; Peter Sunehag; Marcus Hutter (EWRL, 2012)

Q-learning for history-based reinforcement learning. - Mayank Daswani; Peter Sunehag; Marcus Hutter (ACML, 2013)

Learning Agents with Evolving Hypothesis Classes. - Peter Sunehag; Marcus Hutter (AGI, 2013)

Concentration and Confidence for Discrete Bayesian Sequence Predictors. - Tor Lattimore; Marcus Hutter; Peter Sunehag (ALT, 2013)

Online feature selection for Brain Computer Interfaces. - Gareth Oliver; Peter Sunehag; Tom Gedeon (CCMB, 2013)

The Sample-Complexity of General Reinforcement Learning. - Tor Lattimore; Marcus Hutter; Peter Sunehag (ICML (3), 2013)

On Nicod's Condition, Rules of Induction and the Raven Paradox. - Hadi Mohasel Afshar; Peter Sunehag (CoRR, 2013)

Reinforcement learning with value advice. - Mayank Daswani; Peter Sunehag; Marcus Hutter (ACML, 2014)

Intelligence as Inference or Forcing Occam on the World. - Peter Sunehag; Marcus Hutter (AGI, 2014)

A Dual Process Theory of Optimistic Cognition. - Peter Sunehag; Marcus Hutter (CogSci, 2014)

Using Localization and Factorization to Reduce the Complexity of Reinforcement Learning. - Peter Sunehag; Marcus Hutter (AGI, 2015)

Reinforcement Learning in Large Discrete Action Spaces. - Gabriel Dulac-Arnold; Richard Evans 001; Peter Sunehag; Ben Coppin (CoRR, 2015)

Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions. - Peter Sunehag; Richard Evans 001; Gabriel Dulac-Arnold; Yori Zwols; Daniel Visentin; Ben Coppin (CoRR, 2015)

Rationality, optimism and guarantees in general reinforcement learning. - Peter Sunehag; Marcus Hutter (J. Mach. Learn. Res., 2015)

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)

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)

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)

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)

Learning to Incentivize Other Learning Agents. - Jiachen Yang; Ang Li; Mehrdad Farajtabar; Peter Sunehag; Edward Hughes 001; Hongyuan Zha (NeurIPS, 2020)

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)

Melting Pot 2.0. - John P. Agapiou; Alexander Sasha Vezhnevets; Edgar A. Duéñez-Guzmán; Jayd Matyas; Yiran Mao; Peter Sunehag; Raphael Köster; Udari Madhushani; Kavya Kopparapu; Ramona Comanescu; DJ Strouse; Michael Bradley Johanson; et al. (CoRR, 2022)

Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition. - Peter Sunehag; Alexander Sasha Vezhnevets; Edgar A. Duéñez-Guzmán; Igor Mordatch; Joel Z. Leibo (AAMAS, 2023)

A Review of Cooperation in Multi-agent Learning. - Yali Du 001; Joel Z. Leibo; Usman Islam; Richard Willis; Peter Sunehag (CoRR, 2023)

A theory of appropriateness with applications to generative artificial intelligence. - Joel Z. Leibo; Alexander Sasha Vezhnevets; Manfred Diaz; John P. Agapiou; William A. Cunningham; Peter Sunehag; Julia Haas; Raphael Koster; Edgar A. Duéñez-Guzmán; William S. Isaac; Georgios Piliouras; Stanley M. Bileschi; et al. (CoRR, 2024)

Simulation Streams: A Programming Paradigm for Controlling Large Language Models and Building Complex Systems with Generative AI. - Peter Sunehag; Joel Z. Leibo (CoRR, 2025)