Michel Besserve

According to our database, Michel Besserve authored at least 39 papers between 2007 and 2024.

Collaborative distances:

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

2024

Controlling for discrete unmeasured confounding in nonlinear causal models.

Patrick Burauel, Frederick Eberhardt, Michel Besserve

CoRR, 2024

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

Independent Mechanism Analysis and the Manifold Hypothesis.

Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf

CoRR, 2023

Targeted Reduction of Causal Models.

Armin Kekic, Bernhard Schölkopf, Michel Besserve

CoRR, 2023

Causal Component Analysis.

Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions.

Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf

Proceedings of the International Conference on Machine Learning, 2023

Structure by Architecture: Structured Representations without Regularization.

Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

ima-vae.

Patrik Reizinger, Luigi Gresele, Jack Brady, Dominik Zietlow, Julius von Kügelgen, Michel Besserve, Georg Martius, Wieland Brendel, Bernhard Schölkopf

Dataset, June, 2022

Causal Feature Selection via Orthogonal Search.

Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve

Trans. Mach. Learn. Res., 2022

Bayesian Information Criterion for Event-based Multi-trial Ensemble data.

Kaidi Shao, Nikos K. Logothetis, Michel Besserve

CoRR, 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

Learning soft interventions in complex equilibrium systems.

Michel Besserve, Bernhard Schölkopf

Proceedings of the Uncertainty in Artificial Intelligence, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Exploring the Latent Space of Autoencoders with Interventional Assays.

Felix Leeb, Stefan Bauer, Michel Besserve, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Function Classes for Identifiable Nonlinear Independent Component Analysis.

Simon Buchholz, Michel Besserve, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations.

Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

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

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.

Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Independent mechanism analysis, a new concept?

Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Theory of Independent Mechanisms for Extrapolation in Generative Models.

Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf

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

2020

Causal learning with sufficient statistics: an information bottleneck approach.

Daniel Chicharro, Michel Besserve, Stefano Panzeri

CoRR, 2020

Counterfactuals uncover the modular structure of deep generative models.

Michel Besserve, Arash Mehrjou, Rémy Sun, Bernhard Schölkopf

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory.

Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schölkopf

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018

Counterfactuals uncover the modular structure of deep generative models.

Michel Besserve, Rémy Sun, Bernhard Schölkopf

CoRR, 2018

Coordination via predictive assistants from a game-theoretic view.

Philipp Geiger, Justus Winkelmann, Claudius Proissl, Michel Besserve, Bernhard Schölkopf

CoRR, 2018

Group invariance principles for causal generative models.

Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2015

Towards Robust and Specific Causal Discovery from fMRI.

Kun Zhang, Biwei Huang, Bernhard Schölkopf, Michel Besserve, Masataka Watanabe, Dajiang Zhu

CoRR, 2015

Telling cause from effect in deterministic linear dynamical systems.

Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve

Proceedings of the 32nd International Conference on Machine Learning, 2015

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

Metabolic Cost as an Organizing Principle for Cooperative Learning.

David Balduzzi, Pedro A. Ortega, Michel Besserve

Adv. Complex Syst., 2013

Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.

Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Towards a learning-theoretic analysis of spike-timing dependent plasticity.

David Balduzzi, Michel Besserve

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

Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view.

Michel Besserve, Jacques Martinerie, Line Garnero

NeuroImage, 2011

Finding dependencies between frequencies with the kernel cross-spectral density.

Michel Besserve, Dominik Janzing, Nikos K. Logothetis, Bernhard Schölkopf

Proceedings of the IEEE International Conference on Acoustics, 2011

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

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

2008

Non-invasive classification of cortical activities for brain computer interface: A variable selection approach.

Michel Besserve, Jacques Martinerie, Line Garnero

Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008

2007

Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machines : un retour aux sources.

Michel Besserve

PhD thesis, 2007