Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machines : un retour aux sources. - Michel Besserve (2007)

Non-invasive classification of cortical activities for brain computer interface: A variable selection approach. - Michel Besserve; Jacques Martinerie; Line Garnero (ISBI, 2008)

Source Reconstruction and Synchrony Measurements for Revealing Functional Brain Networks and Classifying Mental States. - François Laurent; Michel Besserve; Line Garnero; Matthieu Philippe; Geneviève Florence; Jacques Martinerie (Int. J. Bifurc. Chaos, 2010)

Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis. - Michel Besserve; Bernhard Schölkopf; Nikos K. Logothetis; Stefano Panzeri (J. Comput. Neurosci., 2010)

Finding dependencies between frequencies with the kernel cross-spectral density. - Michel Besserve; Dominik Janzing; Nikos K. Logothetis; Bernhard Schölkopf (ICASSP, 2011)

Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view. - Michel Besserve; Jacques Martinerie; Line Garnero (NeuroImage, 2011)

Towards a learning-theoretic analysis of spike-timing dependent plasticity. - David Balduzzi; Michel Besserve (NIPS, 2012)

Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. - Michel Besserve; Nikos K. Logothetis; Bernhard Schölkopf (NIPS, 2013)

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

Multimodal information improves the rapid detection of mental fatigue. - François Laurent; Mario Valderrama; Michel Besserve; Mathias Guillard; Jean-Philippe Lachaux; Jacques Martinerie; Geneviève Florence (Biomed. Signal Process. Control., 2013)

Telling cause from effect in deterministic linear dynamical systems. - Naji Shajarisales; Dominik Janzing; Bernhard Schölkopf; Michel Besserve (ICML, 2015)

Towards Robust and Specific Causal Discovery from fMRI. - Kun Zhang 001; Biwei Huang; Bernhard Schölkopf; Michel Besserve; Masataka Watanabe; Dajiang Zhu (CoRR, 2015)

Group invariance principles for causal generative models. - Michel Besserve; Naji Shajarisales; Bernhard Schölkopf; Dominik Janzing (AISTATS, 2018)

Coordination via predictive assistants from a game-theoretic view. - Philipp Geiger; Justus Winkelmann; Claudius Proissl; Michel Besserve; Bernhard Schölkopf (CoRR, 2018)

Counterfactuals uncover the modular structure of deep generative models. - Michel Besserve; Rémy Sun; Bernhard Schölkopf (CoRR, 2018)

Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. - Philipp Geiger; Michel Besserve; Justus Winkelmann; Claudius Proissl; Bernhard Schölkopf (UAI, 2019)

Counterfactuals uncover the modular structure of deep generative models. - Michel Besserve; Arash Mehrjou; Rémy Sun; Bernhard Schölkopf (ICLR, 2020)

Causal learning with sufficient statistics: an information bottleneck approach. - Daniel Chicharro; Michel Besserve; Stefano Panzeri (CoRR, 2020)

A Theory of Independent Mechanisms for Extrapolation in Generative Models. - Michel Besserve; Rémy Sun; Dominik Janzing; Bernhard Schölkopf (AAAI, 2021)

Independent mechanism analysis, a new concept? - Luigi Gresele; Julius von Kügelgen; Vincent Stimper; Bernhard Schölkopf; Michel Besserve (NeurIPS, 2021)

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. - Julius von Kügelgen; Yash Sharma 001; Luigi Gresele; Wieland Brendel; Bernhard Schölkopf; Michel Besserve; Francesco Locatello (NeurIPS, 2021)

From Univariate to Multivariate Coupling Between Continuous Signals and Point Processes: A Mathematical Framework. - Shervin Safavi; Nikos K. Logothetis; Michel Besserve (Neural Comput., 2021)

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. - Michel Besserve; Naji Shajarisales; Dominik Janzing; Bernhard Schölkopf (CLeaR, 2022)

Function Classes for Identifiable Nonlinear Independent Component Analysis. - Simon Buchholz; Michel Besserve; Bernhard Schölkopf (NeurIPS, 2022)

Exploring the Latent Space of Autoencoders with Interventional Assays. - Felix Leeb; Stefan Bauer; Michel Besserve; Bernhard Schölkopf (NeurIPS, 2022)

Embrace the Gap: VAEs Perform Independent Mechanism Analysis. - Patrik Reizinger; Luigi Gresele; Jack Brady; Julius von Kügelgen; Dominik Zietlow; Bernhard Schölkopf; Georg Martius; Wieland Brendel; Michel Besserve (NeurIPS, 2022)

Learning soft interventions in complex equilibrium systems. - Michel Besserve; Bernhard Schölkopf (UAI, 2022)

On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective". - Shubhangi Ghosh; Luigi Gresele; Julius von Kügelgen; Michel Besserve; Bernhard Schölkopf (CoRR, 2022)

Bayesian Information Criterion for Event-based Multi-trial Ensemble data. - Kaidi Shao; Nikos K. Logothetis; Michel Besserve (CoRR, 2022)

Causal Feature Selection via Orthogonal Search. - Ashkan Soleymani; Anant Raj; Stefan Bauer; Bernhard Schölkopf; Michel Besserve (Trans. Mach. Learn. Res., 2022)

ima-vae. - Patrik Reizinger; Luigi Gresele; Jack Brady; Dominik Zietlow; Julius von Kügelgen; Michel Besserve; Georg Martius; Wieland Brendel; Bernhard Schölkopf (Zenodo, 2022)

Structure by Architecture: Structured Representations without Regularization. - Felix Leeb; Giulia Lanzillotta; Yashas Annadani; Michel Besserve; Stefan Bauer; Bernhard Schölkopf (ICLR, 2023)

Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. - Hamza Keurti; Hsiao-Ru Pan; Michel Besserve; Benjamin F. Grewe; Bernhard Schölkopf (ICML, 2023)

Nonparametric Identifiability of Causal Representations from Unknown Interventions. - Julius von Kügelgen; Michel Besserve; Wendong Liang; Luigi Gresele; Armin Kekic; Elias Bareinboim; David M. Blei; Bernhard Schölkopf (NeurIPS, 2023)

Causal Component Analysis. - Wendong Liang; Armin Kekic; Julius von Kügelgen; Simon Buchholz; Michel Besserve; Luigi Gresele; Bernhard Schölkopf (NeurIPS, 2023)

Independent Mechanism Analysis and the Manifold Hypothesis. - Shubhangi Ghosh; Luigi Gresele; Julius von Kügelgen; Michel Besserve; Bernhard Schölkopf (CoRR, 2023)

Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. - Shervin Safavi; Theofanis I. Panagiotaropoulos; Vishal Kapoor; Juan F. Ramirez-Villegas; Nikos K. Logothetis; Michel Besserve (PLoS Comput. Biol., 2023)

Targeted Reduction of Causal Models. - Armin Kekic; Bernhard Schölkopf; Michel Besserve (UAI, 2024)

Controlling for discrete unmeasured confounding in nonlinear causal models. - Patrick Burauel; Frederick Eberhardt; Michel Besserve (CLeaR, 2025)

Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies. - Armin Kekic; Jan Schneider 007; Dieter Büchler; Bernhard Schölkopf; Michel Besserve (CoRR, 2025)