Strong Normality and Generalized Copeland–Erdős Numbers. - Elliot Catt; Michael Coons; Jordan Velich (Integers, 2016)

A Game-Theoretic Analysis of the Off-Switch Game. - Tobias Wängberg; Mikael Böörs; Elliot Catt; Tom Everitt; Marcus Hutter (AGI, 2017)

A Strongly Asymptotically Optimal Agent in General Environments. - Michael K. Cohen; Elliot Catt; Marcus Hutter (IJCAI, 2019)

Strong Asymptotic Optimality in General Environments. - Michael K. Cohen; Elliot Catt; Marcus Hutter (CoRR, 2019)

A Gentle Introduction to Quantum Computing Algorithms with Applications to Universal Prediction. - Elliot Catt; Marcus Hutter (CoRR, 2020)

On the formalisation of Kolmogorov complexity. - Elliot Catt; Michael Norrish (CPP, 2021)

Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent. - Michael K. Cohen; Elliot Catt; Marcus Hutter (IEEE J. Sel. Areas Inf. Theory, 2021)

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

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

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)

Grandmaster-Level Chess Without Search. - Anian Ruoss; Grégoire Delétang; Sourabh Medapati; Jordi Grau-Moya; Li Kevin Wenliang; Elliot Catt; John Reid; Tim Genewein (CoRR, 2024)

Evaluating Frontier Models for Dangerous Capabilities. - Mary Phuong; Matthew Aitchison; Elliot Catt; Sarah Cogan; Alexandre Kaskasoli; Victoria Krakovna; David Lindner; Matthew Rahtz; Yannis Assael; Sarah Hodkinson; Heidi Howard; Tom Lieberum; et al. (CoRR, 2024)