A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function. - Pedro A. Ortega; Jordi Grau-Moya; Tim Genewein; David Balduzzi; Daniel A. Braun 001 (NIPS, 2012)

Abstraction in decision-makers with limited information processing capabilities. - Tim Genewein; Daniel A. Braun 001 (CoRR, 2013)

Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimality Principle. - Tim Genewein; Felix Leibfried; Jordi Grau-Moya; Daniel Alexander Braun (Frontiers Robotics AI, 2015)

Structure Learning in Bayesian Sensorimotor Integration. - Tim Genewein; Eduard Hez; Zeynab Razzaghpanah; Daniel A. Braun 001 (PLoS Comput. Biol., 2015)

Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. - Jordi Grau-Moya; Felix Leibfried; Tim Genewein; Daniel A. Braun 001 (ECML/PKDD (2), 2016)

Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations. - Tim Genewein; Daniel A. Braun 001 (Biol. Cybern., 2016)

On Detecting Adversarial Perturbations. - Jan Hendrik Metzen; Tim Genewein; Volker Fischer 003; Bastian Bischoff (ICLR (Poster), 2017)

An information-theoretic on-line update principle for perception-action coupling. - Zhen Peng 004; Tim Genewein; Felix Leibfried; Daniel A. Braun 001 (IROS, 2017)

The Power of Ensembles for Active Learning in Image Classification. - William H. Beluch; Tim Genewein; Andreas Nürnberger; Jan M. Köhler (CVPR, 2018)

Variational Network Quantization. - Jan Achterhold; Jan M. Köhler; Anke Schmeink; Tim Genewein (ICLR (Poster), 2018)

Group Pruning Using a Bounded-ℓp Norm for Group Gating and Regularization. - Chaithanya Kumar Mummadi; Tim Genewein; Dan Zhang 003; Thomas Brox; Volker Fischer 003 (GCPR, 2019)

Sinkhorn AutoEncoders. - Giorgio Patrini; Rianne van den Berg; Patrick Forré; Marcello Carioni; Samarth Bhargav 001; Max Welling; Tim Genewein; Frank Nielsen (UAI, 2019)

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)

Group Pruning using a Bounded-Lp norm for Group Gating and Regularization. - Chaithanya Kumar Mummadi; Tim Genewein; Dan Zhang 003; Thomas Brox; Volker Fischer 003 (CoRR, 2019)

Meta-trained agents implement Bayes-optimal agents. - Vladimir Mikulik; Grégoire Delétang; Tom McGrath; Tim Genewein; Miljan Martic; Shane Legg; Pedro A. Ortega (NeurIPS, 2020)

Algorithms for Causal Reasoning in Probability Trees. - Tim Genewein; Tom McGrath; Grégoire Delétang; Vladimir Mikulik; Miljan Martic; Shane Legg; Pedro A. Ortega (CoRR, 2020)

Causal Analysis of Agent Behavior for AI Safety. - Grégoire Delétang; Jordi Grau-Moya; Miljan Martic; Tim Genewein; Tom McGrath; Vladimir Mikulik; Markus Kunesch; Shane Legg; Pedro A. Ortega (CoRR, 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)

Model-Free Risk-Sensitive Reinforcement Learning. - Grégoire Delétang; Jordi Grau-Moya; Markus Kunesch; Tim Genewein; Rob Brekelmans; Shane Legg; Pedro A. Ortega (CoRR, 2021)

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)

Your Policy Regularizer is Secretly an Adversary. - Rob Brekelmans; Tim Genewein; Jordi Grau-Moya; Grégoire Delétang; Markus Kunesch; Shane Legg; Pedro A. Ortega (Trans. Mach. Learn. Res., 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)

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)

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

LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations. - Anian Ruoss; Fabio Pardo; Harris Chan; Bonnie Li; Volodymyr Mnih; Tim Genewein (CoRR, 2024)

Why is prompting hard? Understanding prompts on binary sequence predictors. - Li Kevin Wenliang; Anian Ruoss; Jordi Grau-Moya; Marcus Hutter; Tim Genewein (CoRR, 2025)

Understanding Prompt Tuning and In-Context Learning via Meta-Learning. - Tim Genewein; Li Kevin Wenliang; Jordi Grau-Moya; Anian Ruoss; Laurent Orseau; Marcus Hutter (CoRR, 2025)