Jordi Grau-Moya

46 papers A* 8Misc 1Journal 32Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Generative Model of Conspicuous Consumption and Status Signaling. CoRR Logan Cross, Jordi Grau-Moya, William A. Cunningham, Alexander Sasha Vezhnevets, Joel Z. Leibo
2025	J	`jnl`	Code World Models for General Game Playing. CoRR Wolfgang Lehrach, Daniel Hennes, Miguel Lázaro-Gredilla, Xinghua Lou, Carter Wendelken, Zun Li, Antoine Dedieu, Jordi Grau-Moya, Marc Lanctot, Atil Iscen, John Schultz, Marcus Chiam, Ian Gemp, Piotr Zielinski, Satinder Singh, Kevin P. Murphy
2025	J	`jnl`	LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities. CoRR Thomas Schmied, Jörg Bornschein, Jordi Grau-Moya, Markus Wulfmeier, Razvan Pascanu
2025	J	`jnl`	Partition Tree Weighting for Non-Stationary Stochastic Bandits. CoRR Joel Veness, Marcus Hutter, András György, Jordi Grau-Moya
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`	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	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`	Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow. CoRR John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried
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
2019	A*	`conf`	A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. NeurIPS Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya
2019	J	`jnl`	A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. CoRR Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya
2019	J	`jnl`	Disentangled Skill Embeddings for Reinforcement Learning. CoRR Janith C. Petangoda, Sergio Pascual-Diaz, Vincent Adam, Peter Vrancx, Jordi Grau-Moya
2019	Misc	`conf`	Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. CoRL Felix Leibfried, Jordi Grau-Moya
2019	J	`jnl`	Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. CoRR Felix Leibfried, Jordi Grau-Moya
2019	—	`conf`	Soft Q-Learning with Mutual-Information Regularization. ICLR (Poster) Jordi Grau-Moya, Felix Leibfried, Peter Vrancx
2018	A*	`conf`	Balancing Two-Player Stochastic Games with Soft Q-Learning. IJCAI Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
2018	J	`jnl`	Balancing Two-Player Stochastic Games with Soft Q-Learning. CoRR Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
2018	J	`jnl`	Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments. Entropy Jordi Grau-Moya, Matthias Krüger, Daniel A. Braun
2017	J	`jnl`	An Information-Theoretic Optimality Principle for Deep Reinforcement Learning. CoRR Felix Leibfried, Jordi Grau-Moya, Haitham Bou-Ammar
2017	—	`—`	Decision-Making under Bounded Rationality and Model Uncertainty: an Information-Theoretic Approach Jordi Grau-Moya
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`	Adaptive information-theoretic bounded rational decision-making with parametric priors. CoRR Jordi Grau-Moya, 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
2013	J	`jnl`	Bounded Rational Decision-Making in Changing Environments. CoRR Jordi Grau-Moya, 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
2012	J	`jnl`	Noise-induced up/Down Dynamics in Scale-Free neuronal Networks. Int. J. Bifurc. Chaos Jordi Grau-Moya, Antonio J. Pons Rivero, Jordi García-Ojalvo
2012	J	`jnl`	Risk-Sensitivity in Bayesian Sensorimotor Integration. PLoS Comput. Biol. Jordi Grau-Moya, Pedro A. Ortega, Daniel A. Braun