Pedro A. Ortega

According to our database, Pedro A. Ortega authored at least 52 papers between 2006 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Neural Networks and the Chomsky Hierarchy.

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Your Policy Regularizer is Secretly an Adversary.

Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro A. Ortega

Trans. Mach. Learn. Res., 2022

Beyond Bayes-optimality: meta-learning what you know you don't know.

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

CoRR, 2022

Neural Networks and the Chomsky Hierarchy.

Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega

CoRR, 2022

2021

Model-Free Risk-Sensitive Reinforcement Learning.

Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega

CoRR, 2021

Shaking the foundations: delusions in sequence models for interaction and control.

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

CoRR, 2021

Causal Analysis of Agent Behavior for AI Safety.

Grégoire Delétang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega

CoRR, 2021

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization.

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

Proceedings of the 38th International Conference on Machine Learning, 2021

Agent Incentives: A Causal Perspective.

Tom Everitt, Ryan Carey, Eric D. Langlois, Pedro A. Ortega, Shane Legg

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Algorithms for Causal Reasoning in Probability Trees.

Tim Genewein, Tom McGrath, Grégoire Delétang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega

CoRR, 2020

Action and Perception as Divergence Minimization.

Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl J. Friston, Nicolas Heess

CoRR, 2020

Meta-trained agents implement Bayes-optimal agents.

Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Bayesian optimistic Kullback-Leibler exploration.

Kanghoon Lee, Geon-hyeong Kim, Pedro A. Ortega, Daniel D. Lee, Kee-Eung Kim

Mach. Learn., 2019

Meta reinforcement learning as task inference.

Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess

CoRR, 2019

Meta-learning of Sequential Strategies.

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

CoRR, 2019

Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings.

Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg

CoRR, 2019

Causal Reasoning from Meta-reinforcement Learning.

Ishita Dasgupta, Jane X. Wang, Silvia Chiappa, Jovana Mitrovic, Pedro A. Ortega, David Raposo, Edward Hughes, Peter W. Battaglia, Matthew M. Botvinick, Zeb Kurth-Nelson

CoRR, 2019

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning.

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL.

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

CoRR, 2018

Modeling Friends and Foes.

Pedro A. Ortega, Shane Legg

CoRR, 2018

2017

AI Safety Gridworlds.

Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg

CoRR, 2017

2016

Memory controls time perception and intertemporal choices.

Pedro A. Ortega, Naftali Tishby

CoRR, 2016

Human Decision-Making under Limited Time.

Pedro A. Ortega, Alan A. Stocker

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Bayesian Reinforcement Learning with Behavioral Feedback.

Teakgyu Hong, Jongmin Lee, Kee-Eung Kim, Pedro A. Ortega, Daniel D. Lee

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

Subjectivity, Bayesianism, and causality.

Pedro A. Ortega

Pattern Recognit. Lett., 2015

Information-Theoretic Bounded Rationality.

Pedro A. Ortega, Daniel A. Braun, Justin Dyer, Kee-Eung Kim, Naftali Tishby

CoRR, 2015

Belief flows for robust online learning.

Pedro A. Ortega, Koby Crammer, Daniel D. Lee

Proceedings of the 2015 Information Theory and Applications Workshop, 2015

Causal reasoning in a prediction task with hidden causes.

Pedro A. Ortega, Daniel D. Lee, Alan A. Stocker

Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 2015

Reactive bandits with attitude.

Pedro A. Ortega, Kee-Eung Kim, Daniel D. Lee

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Information-Theoretic Bounded Rationality and ε-Optimality.

Daniel A. Braun, Pedro A. Ortega

Entropy, 2014

Erratum to: Generalized Thompson sampling for sequential decision-making and causal inference.

Pedro A. Ortega, Daniel A. Braun

Complex Adapt. Syst. Model., 2014

Generalized Thompson sampling for sequential decision-making and causal inference.

Pedro A. Ortega, Daniel A. Braun

Complex Adapt. Syst. Model., 2014

Monte Carlo methods for exact & efficient solution of the generalized optimality equations.

Pedro A. Ortega, Daniel A. Braun, Naftali Tishby

Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

An Adversarial Interpretation of Information-Theoretic Bounded Rationality.

Pedro A. Ortega, Daniel D. Lee

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Metabolic Cost as an Organizing Principle for Cooperative Learning.

David Balduzzi, Pedro A. Ortega, Michel Besserve

Adv. Complex Syst., 2013

2012

Risk-Sensitivity in Bayesian Sensorimotor Integration.

Jordi Grau-Moya, Pedro A. Ortega, Daniel A. Braun

PLoS Comput. Biol., 2012

Free Energy and the Generalized Optimality Equations for Sequential Decision Making

Pedro A. Ortega, Daniel A. Braun

CoRR, 2012

A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function.

Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Bayesian Causal Induction

Pedro A. Ortega

CoRR, 2011

Information, Utility & Bounded Rationality

Pedro A. Ortega, Daniel A. Braun

CoRR, 2011

Reinforcement Learning and the Bayesian Control Rule.

Pedro Alejandro Ortega, Daniel Alexander Braun, Simon J. Godsill

Proceedings of the Artificial General Intelligence - 4th International Conference, 2011

Information, Utility and Bounded Rationality.

Daniel Alexander Braun, Pedro Alejandro Ortega

Proceedings of the Artificial General Intelligence - 4th International Conference, 2011

Path integral control and bounded rationality.

Daniel A. Braun, Pedro A. Ortega, Evangelos A. Theodorou, Stefan Schaal

Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning, 2011

2010

A Minimum Relative Entropy Principle for Learning and Acting.

Pedro A. Ortega, Daniel A. Braun

J. Artif. Intell. Res., 2010

An axiomatic formalization of bounded rationality based on a utility-information equivalence

Pedro A. Ortega, Daniel A. Braun

CoRR, 2010

Convergence of Bayesian Control Rule

Pedro A. Ortega, Daniel A. Braun

CoRR, 2010

A Minimum Relative Entropy Controller for Undiscounted Markov Decision Processes

Pedro A. Ortega, Daniel A. Braun

CoRR, 2010

A Minimum Relative Entropy Principle for Adaptive Control in Linear Quadratic Regulators.

Daniel A. Braun, Pedro A. Ortega

Proceedings of the ICINCO 2010, 2010

2009

Nash Equilibria in Multi-Agent Motor Interactions.

Daniel A. Braun, Pedro A. Ortega, Daniel M. Wolpert

PLoS Comput. Biol., 2009

A conversion between utility and information

Pedro A. Ortega, Daniel A. Braun

CoRR, 2009

A Bayesian Rule for Adaptive Control based on Causal Interventions

Pedro A. Ortega, Daniel A. Braun

CoRR, 2009

2006

A Medical Claim Fraud/Abuse Detection System based on Data Mining: A Case Study in Chile.

Pedro A. Ortega, Cristián J. Figueroa, Gonzalo A. Ruz

Proceedings of the 2006 International Conference on Data Mining, 2006