Pedro A. Ortega

Publication Activity (10 Years)

Years Active: 2006-2023
Publications (10 Years): 29

Top Topics

Causal Inference

Reinforcement Learning

Monte Carlo Methods

Top Venues

Complex Adapt. Syst. Model.

Publications

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)
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 (2022)
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Gregoire Detetang, Markus Kunesch, Shane Legg, Pedro A. Ortega
Your Policy Regularizer is Secretly an Adversary. Trans. Mach. Learn. Res. 2022 (2022)
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega
Your Policy Regularizer is Secretly an Adversary. CoRR (2022)
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 (2022)
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 (2021)
Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega
Model-Free Risk-Sensitive Reinforcement Learning. CoRR (2021)
Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg
Agent Incentives: A Causal Perspective. CoRR (2021)
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 (2021)
Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML (2021)
Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg
Agent Incentives: A Causal Perspective. AAAI (2021)
Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl J. Friston, Nicolas Heess
Action and Perception as Divergence Minimization. CoRR (2020)
Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega
Algorithms for Causal Reasoning in Probability Trees. CoRR (2020)
Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
Meta-trained agents implement Bayes-optimal agents. CoRR (2020)
Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
Meta-trained agents implement Bayes-optimal agents. NeurIPS (2020)
Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR (2020)
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. ICML (2019)
Kanghoon Lee, Geon-hyeong Kim, Pedro A. Ortega, Daniel D. Lee, Kee-Eung Kim
Bayesian optimistic Kullback-Leibler exploration. Mach. Learn. 108 (5) (2019)
Ishita Dasgupta, Jane X. Wang, Silvia Chiappa, Jovana Mitrovic, Pedro A. Ortega, David Raposo, Edward Hughes, Peter W. Battaglia, Matthew Botvinick, Zeb Kurth-Nelson
Causal Reasoning from Meta-reinforcement Learning. CoRR (2019)
Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess
Meta reinforcement learning as task inference. CoRR (2019)
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 Botvinick, Shane Legg
Meta-learning of Sequential Strategies. CoRR (2019)
Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings. CoRR (2019)
Pedro A. Ortega, Shane Legg
Modeling Friends and Foes. CoRR (2018)
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL. CoRR (2018)
Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg
AI Safety Gridworlds. CoRR (2017)
Teakgyu Hong, Jongmin Lee, Kee-Eung Kim, Pedro A. Ortega, Daniel D. Lee
Bayesian Reinforcement Learning with Behavioral Feedback. IJCAI (2016)
Pedro A. Ortega, Alan A. Stocker
Human Decision-Making under Limited Time. CoRR (2016)
Pedro A. Ortega, Naftali Tishby
Memory controls time perception and intertemporal choices. CoRR (2016)
Pedro A. Ortega, Alan A. Stocker
Human Decision-Making under Limited Time. NIPS (2016)
Pedro A. Ortega, Daniel D. Lee, Alan A. Stocker
Causal reasoning in a prediction task with hidden causes. CogSci (2015)
Pedro A. Ortega, Koby Crammer, Daniel D. Lee
Belief flows for robust online learning. ITA (2015)
Pedro A. Ortega, Daniel A. Braun, Justin Dyer, Kee-Eung Kim, Naftali Tishby
Information-Theoretic Bounded Rationality. CoRR (2015)
Pedro A. Ortega, Kee-Eung Kim, Daniel D. Lee
Reactive bandits with attitude. AISTATS (2015)
Pedro A. Ortega
Subjectivity, Bayesianism, and causality. Pattern Recognit. Lett. 64 (2015)
Pedro A. Ortega, Koby Crammer, Daniel D. Lee
Belief Flows of Robust Online Learning. CoRR (2015)
Daniel A. Braun, Pedro A. Ortega
Information-Theoretic Bounded Rationality and ε-Optimality. Entropy 16 (8) (2014)
Pedro A. Ortega, Daniel D. Lee
An Adversarial Interpretation of Information-Theoretic Bounded Rationality. CoRR (2014)
Pedro A. Ortega, Daniel A. Braun
Erratum to: Generalized Thompson sampling for sequential decision-making and causal inference. Complex Adapt. Syst. Model. 2 (2014)
Pedro A. Ortega, Daniel A. Braun, Naftali Tishby
Monte Carlo methods for exact & efficient solution of the generalized optimality equations. ICRA (2014)
Pedro A. Ortega, Daniel D. Lee
An Adversarial Interpretation of Information-Theoretic Bounded Rationality. AAAI (2014)
Pedro A. Ortega, Daniel A. Braun
Generalized Thompson sampling for sequential decision-making and causal inference. Complex Adapt. Syst. Model. 2 (2014)
Pedro A. Ortega
Subjectivity, Bayesianism, and Causality. CoRR (2014)
Pedro A. Ortega, Daniel A. Braun
Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference CoRR (2013)
David Balduzzi, Pedro A. Ortega, Michel Besserve
Metabolic Cost as an Organizing Principle for Cooperative Learning. Adv. Complex Syst. 16 (2-3) (2013)
David Balduzzi, Pedro A. Ortega, Michel Besserve
Metabolic cost as an organizing principle for cooperative learning CoRR (2012)
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)
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 (2012)
Pedro A. Ortega, Daniel A. Braun
Free Energy and the Generalized Optimality Equations for Sequential Decision Making CoRR (2012)
Jordi Grau-Moya, Pedro A. Ortega, Daniel A. Braun
Risk-Sensitivity in Bayesian Sensorimotor Integration. PLoS Comput. Biol. 8 (9) (2012)
Pedro A. Ortega
Bayesian Causal Induction CoRR (2011)
Pedro A. Ortega, Daniel A. Braun
Information, Utility & Bounded Rationality CoRR (2011)
Daniel A. Braun, Pedro A. Ortega, Evangelos A. Theodorou, Stefan Schaal
Path integral control and bounded rationality. ADPRL (2011)
Pedro A. Ortega, Daniel A. Braun
A Minimum Relative Entropy Principle for Learning and Acting. J. Artif. Intell. Res. 38 (2010)
Pedro A. Ortega, Daniel A. Braun
A Minimum Relative Entropy Controller for Undiscounted Markov Decision Processes CoRR (2010)
Daniel A. Braun, Pedro A. Ortega
A Minimum Relative Entropy Principle for Adaptive Control in Linear Quadratic Regulators. ICINCO (3) (2010)
Pedro A. Ortega, Daniel A. Braun
Convergence of Bayesian Control Rule CoRR (2010)
Pedro A. Ortega, Daniel A. Braun
An axiomatic formalization of bounded rationality based on a utility-information equivalence CoRR (2010)
Pedro A. Ortega, Daniel A. Braun
A Bayesian Rule for Adaptive Control based on Causal Interventions CoRR (2009)
Pedro A. Ortega, Daniel A. Braun
A conversion between utility and information CoRR (2009)
Daniel A. Braun, Pedro A. Ortega, Daniel M. Wolpert
Nash Equilibria in Multi-Agent Motor Interactions. PLoS Comput. Biol. 5 (8) (2009)
Pedro A. Ortega, Daniel A. Braun
A Minimum Relative Entropy Principle for Learning and Acting CoRR (2008)
Pedro A. Ortega, Cristián J. Figueroa, Gonzalo A. Ruz
A Medical Claim Fraud/Abuse Detection System based on Data Mining: A Case Study in Chile. DMIN (2006)