Tim Genewein - RankMe

47 papers A* 10A 2Journal 29Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data. ICML David Heurtel-Depeiges, Anian Ruoss, Joel Veness, Tim Genewein
2025	A*	`conf`	LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations. ICML Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, Tim Genewein
2025	J	`jnl`	Understanding Prompt Tuning and In-Context Learning via Meta-Learning. CoRR Tim Genewein, Li Kevin Wenliang, Jordi Grau-Moya, Anian Ruoss, Laurent Orseau, Marcus Hutter
2025	J	`jnl`	Why is prompting hard? Understanding prompts on binary sequence predictors. CoRR Li Kevin Wenliang, Anian Ruoss, Jordi Grau-Moya, Marcus Hutter, Tim Genewein
2024	A*	`conf`	Amortized Planning with Large-Scale Transformers: A Case Study on Chess. NeurIPS Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Kevin Li, Elliot Catt, John Reid, Cannada A. Lewis, Joel Veness, Tim Genewein
2024	J	`jnl`	Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data. CoRR David Heurtel-Depeiges, Anian Ruoss, Joel Veness, Tim Genewein
2024	J	`jnl`	Grandmaster-Level Chess Without Search. CoRR Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li Kevin Wenliang, Elliot Catt, John Reid, Tim Genewein
2024	J	`jnl`	LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations. CoRR Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, Tim Genewein
2024	A*	`conf`	Language Modeling Is Compression. ICLR 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
2024	A*	`conf`	Learning Universal Predictors. ICML Jordi Grau-Moya, Tim Genewein, Marcus Hutter, Laurent Orseau, Grégoire Delétang, Elliot Catt, Anian Ruoss, Li Kevin Wenliang, Christopher Mattern, Matthew Aitchison, Joel Veness
2024	J	`jnl`	Learning Universal Predictors. CoRR Jordi Grau-Moya, Tim Genewein, Marcus Hutter, Laurent Orseau, Grégoire Delétang, Elliot Catt, Anian Ruoss, Li Kevin Wenliang, Christopher Mattern, Matthew Aitchison, Joel Veness
2023	J	`jnl`	Language Modeling Is Compression. CoRR 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
2023	A*	`conf`	Memory-Based Meta-Learning on Non-Stationary Distributions. ICML Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness
2023	J	`jnl`	Memory-Based Meta-Learning on Non-Stationary Distributions. CoRR Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness
2023	A*	`conf`	Neural Networks and the Chomsky Hierarchy. ICLR 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
2023	—	`conf`	Randomized Positional Encodings Boost Length Generalization of Transformers. ACL (2) Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
2023	J	`jnl`	Randomized Positional Encodings Boost Length Generalization of Transformers. CoRR Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness
2023	A*	`conf`	Self-Predictive Universal AI. NeurIPS Elliot Catt, Jordi Grau-Moya, Marcus Hutter, Matthew Aitchison, Tim Genewein, Grégoire Delétang, Kevin Li, Joel Veness
2022	J	`jnl`	Beyond Bayes-optimality: meta-learning what you know you don't know. CoRR 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, Shane Legg, Pedro A. Ortega
2022	J	`jnl`	Neural Networks and the Chomsky Hierarchy. CoRR Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega
2022	J	`jnl`	Your Policy Regularizer is Secretly an Adversary. CoRR Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega
2022	J	`jnl`	Your Policy Regularizer is Secretly an Adversary. Trans. Mach. Learn. Res. Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega
2021	J	`jnl`	Causal Analysis of Agent Behavior for AI Safety. CoRR Grégoire Delétang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega
2021	J	`jnl`	Model-Free Risk-Sensitive Reinforcement Learning. CoRR Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega
2021	J	`jnl`	Shaking the foundations: delusions in sequence models for interaction and control. CoRR 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, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg
2020	J	`jnl`	Algorithms for Causal Reasoning in Probability Trees. CoRR Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega
2020	A*	`conf`	Meta-trained agents implement Bayes-optimal agents. NeurIPS Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
2020	J	`jnl`	Meta-trained agents implement Bayes-optimal agents. CoRR Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
2019	—	`conf`	Group Pruning Using a Bounded-ℓ GCPR Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang, Thomas Brox, Volker Fischer
2019	J	`jnl`	Group Pruning using a Bounded-Lp norm for Group Gating and Regularization. CoRR Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang, Thomas Brox, Volker Fischer
2019	J	`jnl`	Meta-learning of Sequential Strategies. CoRR Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg
2019	A	`conf`	Sinkhorn AutoEncoders. UAI Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
2018	J	`jnl`	An information-theoretic on-line update principle for perception-action coupling. CoRR Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun
2018	J	`jnl`	Sinkhorn AutoEncoders. CoRR Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen
2018	A*	`conf`	The Power of Ensembles for Active Learning in Image Classification. CVPR William H. Beluch, Tim Genewein, Andreas Nürnberger, Jan M. Köhler
2018	—	`conf`	Variational Network Quantization. ICLR (Poster) Jan Achterhold, Jan M. Köhler, Anke Schmeink, Tim Genewein
2017	A	`conf`	An information-theoretic on-line update principle for perception-action coupling. IROS Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun
2017	—	`conf`	On Detecting Adversarial Perturbations. ICLR (Poster) Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff
2017	J	`jnl`	On Detecting Adversarial Perturbations. CoRR Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff
2016	J	`jnl`	Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations. Biol. Cybern. Tim Genewein, Daniel A. Braun
2016	—	`conf`	Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. ECML/PKDD (2) Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun
2016	J	`jnl`	Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. CoRR Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun
2015	J	`jnl`	Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimality Principle. Frontiers Robotics AI Tim Genewein, Felix Leibfried, Jordi Grau-Moya, Daniel Alexander Braun
2015	J	`jnl`	Structure Learning in Bayesian Sensorimotor Integration. PLoS Comput. Biol. Tim Genewein, Eduard Hez, Zeynab Razzaghpanah, Daniel A. Braun
2013	J	`jnl`	Abstraction in decision-makers with limited information processing capabilities. CoRR Tim Genewein, Daniel A. Braun
2012	J	`jnl`	A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function CoRR Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun
2012	—	`conf`	A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function. NIPS Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun