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2020 – today
- 2025
[i24]Li Kevin Wenliang, Anian Ruoss, Jordi Grau-Moya, Marcus Hutter, Tim Genewein:
Why is prompting hard? Understanding prompts on binary sequence predictors. CoRR abs/2502.10760 (2025)- 2024
- [c16]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:
Language Modeling Is Compression. ICLR 2024 - [c15]Jordi Grau-Moya, Tim Genewein, Marcus Hutter, Laurent Orseau, Grégoire Delétang, Elliot Catt, Anian Ruoss, Li Kevin Wenliang, Christopher Mattern, Matthew Aitchison, Joel Veness:
Learning Universal Predictors. ICML 2024 - [c14]Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Kevin Li, Elliot Catt, John Reid, Cannada Lewis, Joel Veness, Tim Genewein:
Amortized Planning with Large-Scale Transformers: A Case Study on Chess. NeurIPS 2024 - [i23]Jordi Grau-Moya, Tim Genewein, Marcus Hutter, Laurent Orseau, Grégoire Delétang, Elliot Catt, Anian Ruoss, Li Kevin Wenliang, Christopher Mattern, Matthew Aitchison, Joel Veness:
Learning Universal Predictors. CoRR abs/2401.14953 (2024) - [i22]Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li Kevin Wenliang, Elliot Catt, John Reid, Tim Genewein:
Grandmaster-Level Chess Without Search. CoRR abs/2402.04494 (2024) - [i21]David Heurtel-Depeiges, Anian Ruoss, Joel Veness, Tim Genewein:
Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data. CoRR abs/2410.05078 (2024) - [i20]Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, Tim Genewein:
LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations. CoRR abs/2412.01441 (2024) - 2023
- [c13]Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness:
Randomized Positional Encodings Boost Length Generalization of Transformers. ACL (2) 2023: 1889-1903 - [c12]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:
Neural Networks and the Chomsky Hierarchy. ICLR 2023 - [c11]Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness:
Memory-Based Meta-Learning on Non-Stationary Distributions. ICML 2023: 11173-11195 - [c10]Elliot Catt, Jordi Grau-Moya, Marcus Hutter, Matthew Aitchison, Tim Genewein, Grégoire Delétang, Kevin Li, Joel Veness:
Self-Predictive Universal AI. NeurIPS 2023 - [i19]Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness:
Memory-Based Meta-Learning on Non-Stationary Distributions. CoRR abs/2302.03067 (2023) - [i18]Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, Joel Veness:
Randomized Positional Encodings Boost Length Generalization of Transformers. CoRR abs/2305.16843 (2023) - [i17]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:
Language Modeling Is Compression. CoRR abs/2309.10668 (2023) - 2022
- [j4]Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega:
Your Policy Regularizer is Secretly an Adversary. Trans. Mach. Learn. Res. 2022 (2022) - [i16]Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega:
Your Policy Regularizer is Secretly an Adversary. CoRR abs/2203.12592 (2022) - [i15]Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega:
Neural Networks and the Chomsky Hierarchy. CoRR abs/2207.02098 (2022) - [i14]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:
Beyond Bayes-optimality: meta-learning what you know you don't know. CoRR abs/2209.15618 (2022) - 2021
- [i13]Grégoire Delétang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega:
Causal Analysis of Agent Behavior for AI Safety. CoRR abs/2103.03938 (2021) - [i12]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:
Shaking the foundations: delusions in sequence models for interaction and control. CoRR abs/2110.10819 (2021) - [i11]Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega:
Model-Free Risk-Sensitive Reinforcement Learning. CoRR abs/2111.02907 (2021) - 2020
- [c9]Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega:
Meta-trained agents implement Bayes-optimal agents. NeurIPS 2020 - [i10]Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega:
Meta-trained agents implement Bayes-optimal agents. CoRR abs/2010.11223 (2020) - [i9]Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega:
Algorithms for Causal Reasoning in Probability Trees. CoRR abs/2010.12237 (2020)
2010 – 2019
- 2019
- [c8]Chaithanya Kumar Mummadi
, Tim Genewein, Dan Zhang, Thomas Brox, Volker Fischer:
Group Pruning Using a Bounded-ℓp Norm for Group Gating and Regularization. GCPR 2019: 139-155 - [c7]Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. UAI 2019: 733-743 - [i8]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:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i7]Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang, Thomas Brox, Volker Fischer:
Group Pruning using a Bounded-Lp norm for Group Gating and Regularization. CoRR abs/1908.03463 (2019) - 2018
- [c6]William H. Beluch, Tim Genewein, Andreas Nürnberger, Jan M. Köhler:
The Power of Ensembles for Active Learning in Image Classification. CVPR 2018: 9368-9377 - [c5]Jan Achterhold, Jan M. Köhler, Anke Schmeink, Tim Genewein:
Variational Network Quantization. ICLR (Poster) 2018 - [i6]Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun:
An information-theoretic on-line update principle for perception-action coupling. CoRR abs/1804.05906 (2018) - [i5]Giorgio Patrini, Marcello Carioni, Patrick Forré, Samarth Bhargav, Max Welling, Rianne van den Berg, Tim Genewein, Frank Nielsen:
Sinkhorn AutoEncoders. CoRR abs/1810.01118 (2018) - 2017
- [c4]Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff:
On Detecting Adversarial Perturbations. ICLR (Poster) 2017 - [c3]Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun:
An information-theoretic on-line update principle for perception-action coupling. IROS 2017: 789-796 - [i4]Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff:
On Detecting Adversarial Perturbations. CoRR abs/1702.04267 (2017) - 2016
- [j3]Tim Genewein, Daniel A. Braun:
Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations. Biol. Cybern. 110(2-3): 135-150 (2016) - [c2]Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun:
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. ECML/PKDD (2) 2016: 475-491 - [i3]Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun:
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. CoRR abs/1604.02080 (2016) - 2015
- [j2]Tim Genewein, Felix Leibfried, Jordi Grau-Moya, Daniel Alexander Braun:
Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimality Principle. Frontiers Robotics AI 2: 27 (2015) - [j1]Tim Genewein, Eduard Hez, Zeynab Razzaghpanah, Daniel A. Braun:
Structure Learning in Bayesian Sensorimotor Integration. PLoS Comput. Biol. 11(8) (2015) - 2013
- [i2]Tim Genewein, Daniel A. Braun:
Abstraction in decision-makers with limited information processing capabilities. CoRR abs/1312.4353 (2013) - 2012
- [c1]Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun:
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function. NIPS 2012: 3014-3022 - [i1]Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun:
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function. CoRR abs/1206.1898 (2012)
Coauthor Index
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