Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders. - Nat Dilokthanakul; Pedro A. M. Mediano; Marta Garnelo; Matthew C. H. Lee; Hugh Salimbeni; Kai Arulkumaran; Murray Shanahan (CoRR, 2016)

Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality. - Xerxes D. Arsiwalla; Pedro A. M. Mediano; Paul F. M. J. Verschure (ICCS, 2017)

An Investigation of the Dynamical Transitions in Harmonically Driven Random Networks of Firing-Rate Neurons. - Kyriacos Nikiforou; Pedro A. M. Mediano; Murray Shanahan (Cogn. Comput., 2017)

The Partial Information Decomposition of Generative Neural Network Models. - Tycho M. S. Tax; Pedro A. M. Mediano; Murray Shanahan (Entropy, 2017)

Adaptation Is Not Just Improvement over Time. - Simon McGregor; Pedro A. M. Mediano (Artif. Life, 2018)

Measuring Fitness Effects of Agent-Environment Interactions. - Simon McGregor; Pedro A. M. Mediano (Artif. Life, 2018)

Relational Forward Models for Multi-Agent Learning. - Andrea Tacchetti; H. Francis Song; Pedro A. M. Mediano; Vinícius Flores Zambaldi; Neil C. Rabinowitz; Thore Graepel; Matthew M. Botvinick; Peter W. Battaglia (CoRR, 2018)

An Information-Theoretic Approach to Self-Organisation: Emergence of Complex Interdependencies in Coupled Dynamical Systems. - Fernando Rosas; Pedro A. M. Mediano; Martín Ugarte; Henrik J. Jensen (Entropy, 2018)

Relational Forward Models for Multi-Agent Learning. - Andrea Tacchetti; H. Francis Song; Pedro A. M. Mediano; Vinícius Flores Zambaldi; János Kramár; Neil C. Rabinowitz; Thore Graepel; Matthew M. Botvinick; Peter W. Battaglia (ICLR (Poster), 2019)

Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing. - Leonardo Novelli; Patricia Wollstadt; Pedro A. M. Mediano; Michael Wibral; Joseph T. Lizier (CoRR, 2019)

Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information. - Fernando Rosas; Pedro A. M. Mediano; Michael Gastpar; Henrik J. Jensen (CoRR, 2019)

Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation. - Pedro A. M. Mediano; Anil K. Seth; Adam B. Barrett (Entropy, 2019)

IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks. - Patricia Wollstadt; Joseph T. Lizier; Raul Vicente; Conor Finn; Mario Martínez-Zarzuela; Pedro A. M. Mediano; Leonardo Novelli; Michael Wibral (J. Open Source Softw., 2019)

Learning, compression, and leakage: Minimising classification error via meta-universal compression principles. - Fernando E. Rosas; Pedro A. M. Mediano; Michael Gastpar (ITW, 2020)

Causal Blankets: Theory and Algorithmic Framework. - Fernando E. Rosas; Pedro A. M. Mediano; Martin Biehl 001; Shamil Chandaria; Daniel Polani (IWAI, 2020)

Deep active inference agents using Monte-Carlo methods. - Zafeirios Fountas; Noor Sajid; Pedro A. M. Mediano; Karl J. Friston (NeurIPS, 2020)

An operational information decomposition via synergistic disclosure. - Fernando Rosas; Pedro A. M. Mediano; Borzoo Rassouli; Adam B. Barrett (CoRR, 2020)

Building Proactive Voice Assistants: When and How (not) to Interact. - Ondrej Miksik; I. Munasinghe; J. Asensio-Cubero; S. Reddy Bethi; S.-T. Huang; S. Zylfo; Xuechen Liu; T. Nica; A. Mitrocsak; S. Mezza; Rory Beard; Ruibo Shi; et al. (CoRR, 2020)

Learning, compression, and leakage: Minimizing classification error via meta-universal compression principles. - Fernando E. Rosas; Pedro A. M. Mediano; Michael Gastpar (CoRR, 2020)

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. - Fernando E. Rosas; Pedro A. M. Mediano; Henrik J. Jensen; Anil K. Seth; Adam B. Barrett; Robin L. Carhart-Harris; Daniel Bor (PLoS Comput. Biol., 2020)

High-Order Interdependencies in the Aging Brain. - Marilyn Gatica; Rodrigo Cofré; Pedro A. M. Mediano; Fernando E. Rosas; Patricio Orio; Ibai Díez; Stephan P. Swinnen; Jesús M. Cortés (Brain Connect., 2021)

Disentangling high-order mechanisms and high-order behaviours in complex systems. - Fernando E. Rosas; Pedro A. M. Mediano; Andrea I. Luppi; Thomas F. Varley; Joseph T. Lizier; Sebastiano Stramaglia; Henrik J. Jensen; Daniele Marinazzo (CoRR, 2022)

Metastability, fractal scaling, and synergistic information processing: What phase relationships reveal about intrinsic brain activity. - Fran Hancock; Joana Cabral; Andrea I. Luppi; Fernando E. Rosas; Pedro A. M. Mediano; Ottavia Dipasquale; Federico E. Turkheimer (NeuroImage, 2022)

Psychedelics and schizophrenia: Distinct alterations to Bayesian inference. - Hardik Rajpal; Pedro A. M. Mediano; Fernando E. Rosas; Christopher B. Timmermann; Stefan Brugger; Suresh D. Muthukumaraswamy; Anil K. Seth; Daniel Bor; Robin L. Carhart-Harris; Henrik J. Jensen (NeuroImage, 2022)

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model. - Marilyn Gatica; Fernando E. Rosas; Pedro A. M. Mediano; Ibai Díez; Stephan P. Swinnen; Patricio Orio; Rodrigo Cofré; Jesús M. Cortés (PLoS Comput. Biol., 2022)

A Logarithmic Decomposition for Information. - Keenan J. A. Down; Pedro A. M. Mediano (ISIT, 2023)

Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions. - Zhaolu Liu; Robert L. Peach; Pedro A. M. Mediano; Mauricio Barahona (NeurIPS, 2023)

Dynamical noise can enhance high-order statistical structure in complex systems. - Patricio Orio; Pedro A. M. Mediano; Fernando E. Rosas (CoRR, 2023)

Quantifying synergy and redundancy in multiplex networks. - Andrea I. Luppi; Eckehard Olbrich; Conor Finn; Laura E. Suárez; Fernando E. Rosas; Pedro A. M. Mediano; Jürgen Jost (CoRR, 2023)

Information decomposition reveals hidden high-order contributions to temporal irreversibility. - Andrea I. Luppi; Fernando E. Rosas; Gustavo Deco; Morten L. Kringelbach; Pedro A. M. Mediano (CoRR, 2023)

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness. - Andrea I. Luppi; Joana Cabral; Rodrigo Cofré; Pedro A. M. Mediano; Fernando E. Rosas; Abid Y. Qureshi; Amy Kuceyeski; Enzo Tagliazucchi; Federico Raimondo; Gustavo Deco; James M. Shine; Morten L. Kringelbach; et al. (NeuroImage, 2023)

Reduced emergent character of neural dynamics in patients with a disrupted connectome. - Andrea I. Luppi; Pedro A. M. Mediano; Fernando E. Rosas; Judith Allanson; John D. Pickard; Guy B. Williams; Michael M. Craig; Paola Finoia; Alexander R. D. Peattie; Peter Coppola; David K. Menon; Daniel Bor; et al. (NeuroImage, 2023)

Not with a "zap" but with a "beep": Measuring the origins of perinatal experience. - Joel Frohlich; Tim Bayne; Julia Sophia Crone; Alessandra DallaVecchia; Asger Kirkeby-Hinrup; Pedro A. M. Mediano; Julia Moser; Karolina Talar; Alireza Gharabaghi; Hubert Preissl (NeuroImage, 2023)

Characterising Directed and Undirected Metrics of High-Order Interdependence. - Fernando E. Rosas; Pedro A. M. Mediano; Michael Gastpar (ISIT (Workshops), 2024)

Learning diverse causally emergent representations from time series data. - David McSharry; Christos Kaplanis; Fernando Rosas; Pedro A. M. Mediano (NeurIPS, 2024)

Identifying Nonstationary Causal Structures with High-Order Markov Switching Models. - Carles Balsells Rodas; Yixin Wang; Pedro A. M. Mediano; Yingzhen Li (CoRR, 2024)

A Logarithmic Decomposition and a Signed Measure Space for Entropy. - Keenan J. A. Down; Pedro A. M. Mediano (CoRR, 2024)

Algebraic Representations of Entropy and Fixed-Parity Information Quantities. - Keenan J. A. Down; Pedro A. M. Mediano (CoRR, 2024)

The Fast Möbius Transform: An algebraic approach to information decomposition. - Abel Jansma; Pedro A. M. Mediano; Fernando E. Rosas (CoRR, 2024)

Characterising high-order interdependence via entropic conjugation. - Fernando E. Rosas; Aaron J. Gutknecht; Pedro A. M. Mediano; Michael Gastpar (CoRR, 2024)

Null models for comparing information decomposition across complex systems. - Alberto Liardi; Fernando E. Rosas; Robin L. Carhart-Harris; George Blackburne; Daniel Bor; Pedro A. M. Mediano (CoRR, 2024)

Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks. - Alexandra M. Proca; Fernando E. Rosas; Andrea I. Luppi; Daniel Bor; Matthew Crosby; Pedro A. M. Mediano (PLoS Comput. Biol., 2024)

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics. - Fernando E. Rosas; Diego Candia-Rivera; Andrea I. Luppi; Yike Guo; Pedro A. M. Mediano (Comput. Biol. Medicine, 2024)

From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks. - Clémentine Carla Juliette Dominé; Nicolas Anguita; Alexandra M. Proca; Lukas Braun; Daniel Kunin; Pedro A. M. Mediano; Andrew M. Saxe (ICLR, 2025)

Grokking at the Edge of Numerical Stability. - Lucas Prieto; Melih Barsbey; Pedro A. M. Mediano; Tolga Birdal (ICLR, 2025)

THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing. - Laouen Belloli; Pedro A. M. Mediano; Rodrigo Cofré; Diego Fernández Slezak; Rubén Herzog (CoRR, 2025)

The topology of synergy: linking topological and information-theoretic approaches to higher-order interactions in complex systems. - Thomas F. Varley; Pedro A. M. Mediano; Alice Patania; Josh C. Bongard (CoRR, 2025)

Simple physical systems as a reference for multivariate information dynamics. - Alberto Liardi; Madalina I. Sas; George Blackburne; William J. Knottenbelt; Pedro A. M. Mediano; Henrik Jeldtoft Jensen (CoRR, 2025)

Shannon invariants: A scalable approach to information decomposition. - Aaron J. Gutknecht; Fernando E. Rosas; David A. Ehrlich; Abdullah Makkeh; Pedro A. M. Mediano; Michael Wibral (CoRR, 2025)

A scalable estimator of high-order information in complex dynamical systems. - Alberto Liardi; George Blackburne; Hardik Rajpal; Fernando E. Rosas; Pedro A. M. Mediano (CoRR, 2025)