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 (DMIN, 2006)

A Bayesian Rule for Adaptive Control based on Causal Interventions - Pedro A. Ortega; Daniel A. Braun 001 (CoRR, 2009)

A conversion between utility and information - Pedro A. Ortega; Daniel A. Braun 001 (CoRR, 2009)

Nash Equilibria in Multi-Agent Motor Interactions. - Daniel A. Braun 001; Pedro A. Ortega; Daniel M. Wolpert (PLoS Comput. Biol., 2009)

A Minimum Relative Entropy Principle for Adaptive Control in Linear Quadratic Regulators. - Daniel A. Braun 001; Pedro A. Ortega (ICINCO (3), 2010)

A Minimum Relative Entropy Controller for Undiscounted Markov Decision Processes - Pedro A. Ortega; Daniel A. Braun 001 (CoRR, 2010)

Convergence of Bayesian Control Rule - Pedro A. Ortega; Daniel A. Braun 001 (CoRR, 2010)

An axiomatic formalization of bounded rationality based on a utility-information equivalence - Pedro A. Ortega; Daniel A. Braun 001 (CoRR, 2010)

A Minimum Relative Entropy Principle for Learning and Acting. - Pedro A. Ortega; Daniel A. Braun 001 (J. Artif. Intell. Res., 2010)

Path integral control and bounded rationality. - Daniel A. Braun 001; Pedro A. Ortega; Evangelos A. Theodorou; Stefan Schaal (ADPRL, 2011)

Information, Utility and Bounded Rationality. - Daniel Alexander Braun; Pedro Alejandro Ortega (AGI, 2011)

Reinforcement Learning and the Bayesian Control Rule. - Pedro Alejandro Ortega; Daniel Alexander Braun; Simon J. Godsill (AGI, 2011)

Information, Utility & Bounded Rationality - Pedro A. Ortega; Daniel A. Braun 001 (CoRR, 2011)

Bayesian Causal Induction - Pedro A. Ortega (CoRR, 2011)

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 001 (NIPS, 2012)

Free Energy and the Generalized Optimality Equations for Sequential Decision Making - Pedro A. Ortega; Daniel A. Braun 001 (CoRR, 2012)

Risk-Sensitivity in Bayesian Sensorimotor Integration. - Jordi Grau-Moya; Pedro A. Ortega; Daniel A. Braun 001 (PLoS Comput. Biol., 2012)

Metabolic Cost as an Organizing Principle for Cooperative Learning. - David Balduzzi; Pedro A. Ortega; Michel Besserve (Adv. Complex Syst., 2013)

An Adversarial Interpretation of Information-Theoretic Bounded Rationality. - Pedro A. Ortega; Daniel D. Lee (AAAI, 2014)

Monte Carlo methods for exact & efficient solution of the generalized optimality equations. - Pedro A. Ortega; Daniel A. Braun 001; Naftali Tishby (ICRA, 2014)

Generalized Thompson sampling for sequential decision-making and causal inference. - Pedro A. Ortega; Daniel A. Braun 001 (Complex Adapt. Syst. Model., 2014)

Erratum to: Generalized Thompson sampling for sequential decision-making and causal inference. - Pedro A. Ortega; Daniel A. Braun 001 (Complex Adapt. Syst. Model., 2014)

Information-Theoretic Bounded Rationality and ε-Optimality. - Daniel A. Braun 001; Pedro A. Ortega (Entropy, 2014)

Reactive bandits with attitude. - Pedro A. Ortega; Kee-Eung Kim; Daniel D. Lee (AISTATS, 2015)

Causal reasoning in a prediction task with hidden causes. - Pedro A. Ortega; Daniel D. Lee; Alan A. Stocker (CogSci, 2015)

Belief flows for robust online learning. - Pedro A. Ortega; Koby Crammer; Daniel D. Lee (ITA, 2015)

Information-Theoretic Bounded Rationality. - Pedro A. Ortega; Daniel A. Braun 001; Justin Dyer; Kee-Eung Kim; Naftali Tishby (CoRR, 2015)

Subjectivity, Bayesianism, and causality. - Pedro A. Ortega (Pattern Recognit. Lett., 2015)

Bayesian Reinforcement Learning with Behavioral Feedback. - Teakgyu Hong; Jongmin Lee 004; Kee-Eung Kim; Pedro A. Ortega; Daniel D. Lee (IJCAI, 2016)

Human Decision-Making under Limited Time. - Pedro A. Ortega; Alan A. Stocker (NIPS, 2016)

Memory controls time perception and intertemporal choices. - Pedro A. Ortega; Naftali Tishby (CoRR, 2016)

AI Safety Gridworlds. - Jan Leike; Miljan Martic; Victoria Krakovna; Pedro A. Ortega; Tom Everitt; Andrew Lefrancq; Laurent Orseau; Shane Legg (CoRR, 2017)

Modeling Friends and Foes. - Pedro A. Ortega; Shane Legg (CoRR, 2018)

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL. - Natasha Jaques; Angeliki Lazaridou; Edward Hughes 001; Çaglar Gülçehre; Pedro A. Ortega; DJ Strouse; Joel Z. Leibo; Nando de Freitas (CoRR, 2018)

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. - Natasha Jaques; Angeliki Lazaridou; Edward Hughes 001; Çaglar Gülçehre; Pedro A. Ortega; DJ Strouse; Joel Z. Leibo; Nando de Freitas (ICML, 2019)

Causal Reasoning from Meta-reinforcement Learning. - Ishita Dasgupta 001; Jane X. Wang; Silvia Chiappa; Jovana Mitrovic; Pedro A. Ortega; David Raposo; Edward Hughes 001; Peter W. Battaglia; Matthew M. Botvinick; Zeb Kurth-Nelson (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)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

Meta reinforcement learning as task inference. - Jan Humplik; Alexandre Galashov; Leonard Hasenclever; Pedro A. Ortega; Yee Whye Teh; Nicolas Heess (CoRR, 2019)

Bayesian optimistic Kullback-Leibler exploration. - Kanghoon Lee; Geon-hyeong Kim; Pedro A. Ortega; Daniel D. Lee; Kee-Eung Kim (Mach. Learn., 2019)

Meta-trained agents implement Bayes-optimal agents. - Vladimir Mikulik; Grégoire Delétang; Tom McGrath; Tim Genewein; Miljan Martic; Shane Legg; Pedro A. Ortega (NeurIPS, 2020)

Action and Perception as Divergence Minimization. - Danijar Hafner; Pedro A. Ortega; Jimmy Ba; Thomas Parr; Karl J. Friston; Nicolas Heess (CoRR, 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)

Agent Incentives: A Causal Perspective. - Tom Everitt; Ryan Carey; Eric D. Langlois 002; Pedro A. Ortega; Shane Legg (AAAI, 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 001; Pedro A. Ortega; Neil Burch; Thomas W. Anthony 001; David Balduzzi; Bart De Vylder; Georgios Piliouras; Marc Lanctot; et al. (ICML, 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)

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; et al. (CoRR, 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)

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)

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; et al. (CoRR, 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)

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 (ICLR, 2023)