Effective Use of Transposition Tables in Stochastic Game Tree Search. - Joel Veness; Alan Blair 001 (CIG, 2007)

Bootstrapping from Game Tree Search. - Joel Veness; David Silver; William T. B. Uther; Alan Blair 001 (NIPS, 2009)

A Monte Carlo AIXI Approximation - Joel Veness; Kee Siong Ng; Marcus Hutter; David Silver (CoRR, 2009)

Reinforcement Learning via AIXI Approximation. - Joel Veness; Kee Siong Ng; Marcus Hutter; David Silver (AAAI, 2010)

Monte-Carlo Planning in Large POMDPs. - David Silver; Joel Veness (NIPS, 2010)

An Approximation of the Universal Intelligence Measure. - Shane Legg; Joel Veness (Algorithmic Probability and Friends, 2011)

Variance Reduction in Monte-Carlo Tree Search. - Joel Veness; Marc Lanctot; Michael H. Bowling (NIPS, 2011)

A Monte-Carlo AIXI Approximation. - Joel Veness; Kee Siong Ng; Marcus Hutter; William T. B. Uther; David Silver (J. Artif. Intell. Res., 2011)

Approximate universal artificial intelligence and self-play learning for games. - Joel Veness (2011)

Investigating Contingency Awareness Using Atari 2600 Games. - Marc G. Bellemare; Joel Veness; Michael Bowling (AAAI, 2012)

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

Context Tree Switching. - Joel Veness; Kee Siong Ng; Marcus Hutter; Michael H. Bowling (DCC, 2012)

Sketch-Based Linear Value Function Approximation. - Marc G. Bellemare; Joel Veness; Michael Bowling (NIPS, 2012)

Sparse Sequential Dirichlet Coding - Joel Veness; Marcus Hutter (CoRR, 2012)

Partition Tree Weighting. - Joel Veness; Martha White; Michael Bowling; András György 001 (DCC, 2013)

Bayesian Learning of Recursively Factored Environments. - Marc G. Bellemare; Joel Veness; Michael Bowling (ICML (3), 2013)

Monte Carlo *-Minimax Search. - Marc Lanctot; Abdallah Saffidine; Joel Veness; Christopher Archibald; Mark H. M. Winands (IJCAI, 2013)

The Arcade Learning Environment: An Evaluation Platform for General Agents. - Marc G. Bellemare; Yavar Naddaf; Joel Veness; Michael Bowling (J. Artif. Intell. Res., 2013)

Skip Context Tree Switching. - Marc G. Bellemare; Joel Veness; Erik Talvitie (ICML, 2014)

Online Learning of k-CNF Boolean Functions. - Joel Veness; Marcus Hutter (CoRR, 2014)

Compress and Control. - Joel Veness; Marc G. Bellemare; Marcus Hutter; Alvin Chua; Guillaume Desjardins (AAAI, 2015)

The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract). - Marc G. Bellemare; Yavar Naddaf; Joel Veness; Michael Bowling (IJCAI, 2015)

Online Learning of k-CNF Boolean Functions. - Joel Veness; Marcus Hutter; Laurent Orseau; Marc G. Bellemare (IJCAI, 2015)

Human-level control through deep reinforcement learning. - Volodymyr Mnih; Koray Kavukcuoglu; David Silver; Andrei A. Rusu; Joel Veness; Marc G. Bellemare; Alex Graves; Martin A. Riedmiller; Andreas Fidjeland; Georg Ostrovski; Stig Petersen; Charles Beattie; et al. (Nat., 2015)

The Forget-me-not Process. - Kieran Milan; Joel Veness; James Kirkpatrick; Michael H. Bowling; Anna Koop; Demis Hassabis (NIPS, 2016)

Overcoming catastrophic forgetting in neural networks. - James Kirkpatrick; Razvan Pascanu; Neil C. Rabinowitz; Joel Veness; Guillaume Desjardins; Andrei A. Rusu; Kieran Milan; John Quan; Tiago Ramalho; Agnieszka Grabska-Barwinska; Demis Hassabis; Claudia Clopath; et al. (CoRR, 2016)

Online Learning with Gated Linear Networks. - Joel Veness; Tor Lattimore; Avishkar Bhoopchand; Agnieszka Grabska-Barwinska; Christopher Mattern; Peter Toth (CoRR, 2017)

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract). - Marlos C. Machado; Marc G. Bellemare; Erik Talvitie; Joel Veness; Matthew J. Hausknecht; Michael Bowling (IJCAI, 2018)

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents. - Marlos C. Machado; Marc G. Bellemare; Erik Talvitie; Joel Veness; Matthew J. Hausknecht; Michael Bowling (J. Artif. Intell. Res., 2018)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

Gated Linear Networks. - Joel Veness; Tor Lattimore; Avishkar Bhoopchand; David Budden; Christopher Mattern; Agnieszka Grabska-Barwinska; Peter Toth; Simon Schmitt; Marcus Hutter (CoRR, 2019)

Gaussian Gated Linear Networks. - David Budden; Adam H. Marblestone; Eren Sezener; Tor Lattimore; Gregory Wayne; Joel Veness (NeurIPS, 2020)

Online Learning in Contextual Bandits using Gated Linear Networks. - Eren Sezener; Marcus Hutter; David Budden; Jianan Wang; Joel Veness (NeurIPS, 2020)

A Combinatorial Perspective on Transfer Learning. - Jianan Wang; Eren Sezener; David Budden; Marcus Hutter; Joel Veness (NeurIPS, 2020)

Gated Linear Networks. - Joel Veness; Tor Lattimore; David Budden; Avishkar Bhoopchand; Christopher Mattern; Agnieszka Grabska-Barwinska; Eren Sezener; Jianan Wang; Peter Toth; Simon Schmitt; Marcus Hutter (AAAI, 2021)

Shaking the foundations: delusions in sequence models for interaction and control. - Pedro A. Ortega; Markus Kunesch; Grégoire Delétang; Tim Genewein; Jordi Grau-Moya; Joel Veness; Jonas Buchli; Jonas Degrave; Bilal Piot; Julien Pérolat; Tom Everitt; Corentin Tallec; et al. (CoRR, 2021)

Reinforcement Learning with Information-Theoretic Actuation. - Elliot Catt; Marcus Hutter; Joel Veness (AGI, 2022)

Beyond Bayes-optimality: meta-learning what you know you don't know. - Jordi Grau-Moya; Grégoire Delétang; Markus Kunesch; Tim Genewein; Elliot Catt; Kevin Li; Anian Ruoss; Chris Cundy; Joel Veness; Jane X. Wang; Marcus Hutter; Christopher Summerfield; et al. (CoRR, 2022)

Randomized Positional Encodings Boost Length Generalization of Transformers. - Anian Ruoss; Grégoire Delétang; Tim Genewein; Jordi Grau-Moya; Róbert Csordás; Mehdi Bennani; Shane Legg; Joel Veness (ACL (2), 2023)

Neural Networks and the Chomsky Hierarchy. - Grégoire Delétang; Anian Ruoss; Jordi Grau-Moya; Tim Genewein; Li Kevin Wenliang; Elliot Catt; Chris Cundy; Marcus Hutter; Shane Legg; Joel Veness; Pedro A. Ortega (ICLR, 2023)

Memory-Based Meta-Learning on Non-Stationary Distributions. - Tim Genewein; Grégoire Delétang; Anian Ruoss; Li Kevin Wenliang; Elliot Catt; Vincent Dutordoir; Jordi Grau-Moya; Laurent Orseau; Marcus Hutter; Joel Veness (ICML, 2023)

Self-Predictive Universal AI. - Elliot Catt; Jordi Grau-Moya; Marcus Hutter; Matthew Aitchison; Tim Genewein; Grégoire Delétang; Kevin Li; Joel Veness (NeurIPS, 2023)

Language Modeling Is Compression. - Grégoire Delétang; Anian Ruoss; Paul-Ambroise Duquenne; Elliot Catt; Tim Genewein; Christopher Mattern; Jordi Grau-Moya; Li Kevin Wenliang; Matthew Aitchison; Laurent Orseau; Marcus Hutter; Joel Veness (ICLR, 2024)

Learning Universal Predictors. - Jordi Grau-Moya; Tim Genewein; Marcus Hutter; Laurent Orseau; Grégoire Delétang; Elliot Catt; Anian Ruoss; Li Kevin Wenliang; Christopher Mattern; Matthew Aitchison; Joel Veness (ICML, 2024)

Amortized Planning with Large-Scale Transformers: A Case Study on Chess. - Anian Ruoss; Grégoire Delétang; Sourabh Medapati; Jordi Grau-Moya; Kevin Li; Elliot Catt; John Reid; Cannada Lewis; Joel Veness; Tim Genewein (NeurIPS, 2024)

Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data. - David Heurtel-Depeiges; Anian Ruoss; Joel Veness; Tim Genewein (CoRR, 2024)

Partition Tree Weighting for Non-Stationary Stochastic Bandits. - Joel Veness; Marcus Hutter; András György 001; Jordi Grau-Moya (CoRR, 2025)