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ORCIDJordi Grau-Moya
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2020 – today
- 2024
[c12]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- [c11]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 - [i21]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) - [i20]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) - 2023
- [c10]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 - [c9]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 - [c8]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 - [c7]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
- [j5]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]John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried:
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow. CoRR abs/2103.14407 (2021) - [i11]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) - [i10]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)
2010 – 2019
- 2019
- [c6]Felix Leibfried, Jordi Grau-Moya:
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. CoRL 2019: 360-373 - [c5]Jordi Grau-Moya, Felix Leibfried, Peter Vrancx:
Soft Q-Learning with Mutual-Information Regularization. ICLR (Poster) 2019 - [c4]Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya:
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. NeurIPS 2019: 7867-7878 - [i9]Janith C. Petangoda, Sergio Pascual-Diaz, Vincent Adam, Peter Vrancx, Jordi Grau-Moya:
Disentangled Skill Embeddings for Reinforcement Learning. CoRR abs/1906.09223 (2019) - [i8]Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya:
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. CoRR abs/1907.12392 (2019) - [i7]Felix Leibfried, Jordi Grau-Moya:
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. CoRR abs/1909.05950 (2019) - 2018
- [j4]Jordi Grau-Moya, Matthias Krüger
, Daniel A. Braun:
Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments. Entropy 20(1): 1 (2018) - [c3]Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar:
Balancing Two-Player Stochastic Games with Soft Q-Learning. IJCAI 2018: 268-274 - [i6]Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar:
Balancing Two-Player Stochastic Games with Soft Q-Learning. CoRR abs/1802.03216 (2018) - 2017
- [b1]Jordi Grau-Moya:
Decision-Making under Bounded Rationality and Model Uncertainty: an Information-Theoretic Approach. Tübingen University, Germany, 2017 - [i5]Felix Leibfried, Jordi Grau-Moya, Haitham Bou-Ammar:
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning. CoRR abs/1708.01867 (2017) - 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 - [i4]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
- [j3]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) - [i3]Jordi Grau-Moya, Daniel A. Braun:
Adaptive information-theoretic bounded rational decision-making with parametric priors. CoRR abs/1511.01710 (2015) - 2013
- [i2]Jordi Grau-Moya, Daniel A. Braun:
Bounded Rational Decision-Making in Changing Environments. CoRR abs/1312.6726 (2013) - 2012
- [j2]Jordi Grau-Moya, Antonio J. Pons Rivero, Jordi García-Ojalvo:
Noise-induced up/Down Dynamics in Scale-Free neuronal Networks. Int. J. Bifurc. Chaos 22(7) (2012) - [j1]Jordi Grau-Moya, Pedro A. Ortega, Daniel A. Braun:
Risk-Sensitivity in Bayesian Sensorimotor Integration. PLoS Comput. Biol. 8(9) (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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