The relationship between dynamic programming and active inference: the discrete, finite-horizon case. - Lancelot Da Costa; Noor Sajid; Thomas Parr; Karl J. Friston; Ryan Smith (CoRR, 2020)

Active inference, Bayesian optimal design, and expected utility. - Noor Sajid; Lancelot Da Costa; Thomas Parr; Karl J. Friston (CoRR, 2021)

Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing. - Lancelot Da Costa; Thomas Parr; Biswa Sengupta; Karl J. Friston (Entropy, 2021)

Some Interesting Observations on the Free Energy Principle. - Karl J. Friston; Lancelot Da Costa; Thomas Parr (Entropy, 2021)

Stochastic Chaos and Markov Blankets. - Karl J. Friston; Conor Heins; Kai Ueltzhöffer; Lancelot Da Costa; Thomas Parr (Entropy, 2021)

Memory and Markov Blankets. - Thomas Parr; Lancelot Da Costa; Conor Heins; Maxwell James D. Ramstead; Karl J. Friston (Entropy, 2021)

A Drive towards Thermodynamic Efficiency for Dissipative Structures in Chemical Reaction Networks. - Kai Ueltzhöffer; Lancelot Da Costa; Daniela Cialfi; Karl J. Friston (Entropy, 2021)

Generative Models for Active Vision. - Thomas Parr; Noor Sajid; Lancelot Da Costa; M. Berk Mirza; Karl J. Friston (Frontiers Neurorobotics, 2021)

Sophisticated Inference. - Karl J. Friston; Lancelot Da Costa; Danijar Hafner; Casper Hesp; Thomas Parr (Neural Comput., 2021)

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents. - Alessandro Barp; Lancelot Da Costa; Guilherme França; Karl J. Friston; Mark A. Girolami; Michael I. Jordan; Grigorios A. Pavliotis (CoRR, 2022)

Modelling non-reinforced preferences using selective attention. - Noor Sajid; Panagiotis Tigas; Zafeirios Fountas; Qinghai Guo; Alexey Zakharov; Lancelot Da Costa (CoRR, 2022)

The entropy production of stationary diffusions. - Lancelot Da Costa; Grigorios A. Pavliotis (CoRR, 2022)

How Active Inference Could Help Revolutionise Robotics. - Lancelot Da Costa; Pablo Lanillos; Noor Sajid; Karl J. Friston; Shujhat Khan (Entropy, 2022)

Bayesian Brains and the Rényi Divergence. - Noor Sajid; Francesco Faccio; Lancelot Da Costa; Thomas Parr; Jürgen Schmidhuber; Karl J. Friston (Neural Comput., 2022)

Branching Time Active Inference: The theory and its generality. - Théophile Champion; Lancelot Da Costa; Howard Bowman; Marek Grzes (Neural Networks, 2022)

Collective behavior from surprise minimization. - Conor Heins; Beren Millidge; Lancelot Da Costa; Richard Mann; Karl J. Friston; Iain D. Couzin (CoRR, 2023)

Supervised structure learning. - Karl J. Friston; Lancelot Da Costa; Alexander Tschantz; Alex B. Kiefer; Tommaso Salvatori; Victorita Neacsu; Magnus T. Koudahl; Conor Heins; Noor Sajid; Dimitrije Markovic; Thomas Parr; Tim Verbelen; et al. (CoRR, 2023)

Sample Path Regularity of Gaussian Processes from the Covariance Kernel. - Nathaël Da Costa; Marvin Pförtner; Lancelot Da Costa; Philipp Hennig (CoRR, 2023)

Reward Maximization Through Discrete Active Inference. - Lancelot Da Costa; Noor Sajid; Thomas Parr; Karl J. Friston; Ryan Smith (Neural Comput., 2023)

Active Inference as a Model of Agency. - Lancelot Da Costa; Samuel Tenka; Dominic Zhao; Noor Sajid (CoRR, 2024)

From pixels to planning: scale-free active inference. - Karl J. Friston; Conor Heins; Tim Verbelen; Lancelot Da Costa; Tommaso Salvatori; Dimitrije Markovic; Alexander Tschantz; Magnus T. Koudahl; Christopher Buckley 001; Thomas Parr (CoRR, 2024)

Toward Universal and Interpretable World Models for Open-ended Learning Agents. - Lancelot Da Costa (CoRR, 2024)

Possible principles for aligned structure learning agents. - Lancelot Da Costa; Tomas Gavenciak; David Hyland; Mandana Samiei; Cristian Dragos-Manta; Candice Pattisapu; Adeel Razi; Karl J. Friston (CoRR, 2024)

On efficient computation in active inference. - Aswin Paul; Noor Sajid; Lancelot Da Costa; Adeel Razi (Expert Syst. Appl., 2024)

Sustainability under Active Inference. - Mahault Albarracin; Maxwell Ramstead; Riddhi J. Pitliya; Inês Hipólito; Lancelot Da Costa; Maria Raffa; Axel Constant; Sarah Grace Manski (Syst., 2024)

Gradient-Free De Novo Learning. - Karl J. Friston; Thomas Parr; Conor Heins; Lancelot Da Costa; Tommaso Salvatori; Alexander Tschantz; Magnus T. Koudahl; Toon Van de Maele; Christopher Buckley 001; Tim Verbelen (Entropy, 2025)

Bayesian Predictive Coding. - Alexander Tschantz; Magnus T. Koudahl; Hampus Linander; Lancelot Da Costa; Conor Heins; Jeff Beck; Christopher L. Buckley (CoRR, 2025)