Michel Besserve

About

Artificial intelligence (AI) can help humans solve hard problems by harnessing vast amounts of data. However, current AI tools may not reliably behave when deployed in the context of real-world complex systems. This is because such systems undergo transformations that are difficult to comprehend and anticipate. We build causal machine learning tools, uncovering the internal structure and transformations of complex artificial, physical and socioeconomic systems. We demonstrate the potential of these tools for promoting trustworthy and interpretable AI.

Publikationen

Selection by topic

Machine Learning and Causality

Target Reduction of Causal Models, A. Kekić, B. Schölkopf and M. Besserve. arXiv preprint.

Information theoretic measures of causal influences during transient neural events, K. Shao, N. K. Logothetis and M. Besserve. Frontiers in Network Physiology, Section Information Theory 2023.

Embrace the Gap: VAEs Perform Independent Mechanism Analysis, P. Reizinger, L. Gresele, J. Brady, J. von Kügelgen, D. Zietlow, B. Schölkopf, G. Martius, W. Brendel, M. Besserve.  NeurIPS 2022.

Learning soft interventions in complex equilibrium systems, M. Besserve and B. Schölkopf, UAI 2022 (oral).

Independent mechanism analysis, a new concept? L. Gresele, J. von Kügelgen, Vincent Stimper, Bernhard Schölkopf and M. Besserve, NeurIPS 2021.

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve and Francesco Locatello, NeurIPS 2021.

A theory of independent mechanisms for extrapolation in generative models, M. Besserve, R. Sun, D. Janzing and B. Schölkopf, AAAI-2021.

Counterfactuals uncover the modular structure of deep generative models, M. Besserve, A. Merhjou, R. Sun and B. Schölkopf, ICLR 2020.

Group invariance principles for causal generative models, M. Besserve, N. Shajarisales, B. Schölkopf and D. Janzing, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018.

Statistics and Stochastic Processes

Function Classes for Identifiable Nonlinear Independent Component Analysis, S. Buchholz, M. Besserve and B. Schölkopf.  NeurIPS 2022.

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations, M. Besserve, N. Shajarisales, D. Janzing, B. Schölkopf CLeaR 2022.

From univariate to multivariate coupling between continuous signals and point processes: a mathematical framework, S. Safavi, N. K. Logothetis and M. Besserve. Neural Computation 2021.

Compuational and Systems Neuroscience

Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis, S. Safavi, T. I. Panagiotaropoulos, V. Kapoor, J. F. Ramirez-Villegas, N. K. Logothetis, M. Besserve. PLoS Computational Biology (accepted).

Coupling of hippocampal theta and ripples with pontogeniculooccipital waves, J. F. Ramirez-Villegas, M. Besserve, Y. Murayama, H. C. Evrard, A. Oeltermann, N. K. Logothetis. Nature 2020.

Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples, J. F. Ramirez-Villegas, K. F. Willeke, N. K. Logothetis and M. Besserve. Neuron 2018; 100:1016-19.

Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events, J. F. Ramirez-Villegas, N. K. Logothetis, M. Besserve. Proceedings of the National Academy of Sciences U.S.A 2015; 112:E6379-E6387.

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer, M. Besserve, S. C. Lowe, N. K. Logothetis, B. Schölkopf, S. Panzeri. PLOS Biology 2015; 13, e1002257.

Collective and Multi-agent Systems

Coordination via predictive assistants: time series algorithms and game-theoretic analysis, P. Geiger, M. Besserve, J. Winkelmann, C. Proissl and B. Schölkopf. UAI 2019.

All topics (by publication type)

Preprints

Target Reduction of Causal Models, A. Kekić, B. Schölkopf and M. Besserve. arXiv preprint.

Conference papers

Nonparametric Identifiability of Causal Representations from Unknown Interventions, J. von Kügelgen, M. Besserve, W. Liang, L. Gresele, A. Kekić, E. Bareinboim, D. M. Blei and B. Schölkopf. NeurIPS 2023.

Causal Component Analysis, W. Liang, A. Kekić, J. von Kügelgen, S. Buchholz, M. Besserve, L. Gresele and B. Schölkopf, NeurIPS 2023.

Homomorphism AutoEncoder — Learning Group Structured Representations from Observed Transitions, H. Keurti, H. Pan, M. Besserve, B. F. Grewe and B. Schölkopf, ICML 2023.

Structure by Architecture: Structured Representations without Regularization, F. Leeb, G. Lanzillotta, Y. Annadani, M. Besserve, S. Bauer and B. Schölkopf, ICLR 2023.

Embrace the Gap: VAEs Perform Independent Mechanism Analysis, P. Reizinger, L. Gresele, J. Brady, J. von Kügelgen, D. Zietlow, B. Schölkopf, G. Martius, W. Brendel, M. Besserve.  NeurIPS 2022.

Function Classes for Identifiable Nonlinear Independent Component Analysis, S. Buchholz, M. Besserve and B. Schölkopf.  NeurIPS 2022.

Exploring the Latent Space of Autoencoders with Interventional Assays F. Leeb, S. Bauer, M. Besserve and B. Schölkopf. NeurIPS 2022.

Learning soft interventions in complex equilibrium systems, M. Besserve and B. Schölkopf, UAI 2022 (accepted).

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations, M. Besserve, N. Shajarisales, D. Janzing, B. Schölkopf CLeaR 2022.

Independent mechanism analysis, a new concept? L. Gresele, J. von Kügelgen, Vincent Stimper, Bernhard Schölkopf and M. Besserve, NeurIPS 2021.

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve and Francesco Locatello, NeurIPS 2021.

A theory of independent mechanisms for extrapolation in generative models, M. Besserve, R. Sun, D. Janzing and B. Schölkopf. AAAI-2021.

Counterfactuals uncover the modular structure of deep generative models, Besserve, A. Merhjou, R. Sun and B. Schölkopf. ICLR 2020.

Coordination via predictive assistants: time series algorithms and game-theoretic analysis, P. Geiger, M. Besserve, J. Winkelmann, C. Proissl and B. Schölkopf. UAI 2019.

Intrinsic disentanglement: an invariance view for deep generative models, M. Besserve, R. Sun and B. Schölkopf, Workshop on Theoretical Foundations and Applications of Deep Generative Models at ICML 2018.

Group invariance principles for causal generative models, M. Besserve, N. Shajarisales, B. Schölkopf and D. Janzing, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018

Telling cause from effect in deterministic linear dynamical systems, N. Shajarisales, D. Janzing, B. Schölkopf and M. Besserve, ICML 2015.

Statistical analysis of coupled time series in the space of Kernel Cross-Spectral Density operators, M. Besserve, N.K. Logothetis and B. Schölkopf, NIPS 2013.

Towards a learning-theoretic analysis of spike-timing dependent plasticity. D. Balduzzi and M. Besserve, NIPS 2012.

Finding dependencies between frequencies with the kernel cross-spectral density, Besserve, M., D. Janzing, N. K. Logothetis & B. Schölkopf, International Conference on Acoustics, Speech and Signal Processing 2011.

Reconstructing the cortical functional network during imagery tasks for boosting asynchronous BCI, M. Besserve, J. Martinerie & L. Garnero, Second french conference on Computational Neuroscience, “Neurocomp08” 2008.

Non-invasive classification of cortical activities for Brain Computer Interface: A variable selection approach, M. Besserve, J. Martinerie & L. Garnero, 5th IEEE International Symposium on Biomedical Imaging (ISBI) 2008.

De l’estimation à la classification des activités corticales pour les Interfaces Cerveau-Machine, M. Besserve, L. Garnero & J. Martinerie, 21ème colloque GRETSI sur le traitement du signal et des images 2007.

Cross-spectral discriminant analysis for the classification of Brain Computer Interfaces, M. Besserve, L. Garnero & J. Martinerie, 3rd Internationnal IEEE EMBS Conference on Neural Engineering 2007.

Prediction of cognitive states using MEG and Blind Source Separation, M. Besserve, K. Jerbi, L. Garnero & J. Martinerie, Proceedings of the 15th International Conference on Biomagnetism, Vancouver, BC Canada, International Congress Series 2007 ;1300.

Journal Articles

Information theoretic measures of causal influences during transient neural events, K. Shao, N. K. Logothetis and M. Besserve. Frontiers in Network Physiology, Section Information Theory 2023.

Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis, S. Safavi, T. I. Panagiotaropoulos, V. Kapoor, J. F. Ramirez-Villegas, N. K. Logothetis and M. Besserve. PLoS Computational Biology 2023.

Causal Feature Selection via Orthogonal Search, A. Soleymani, A. Raj, S. Bauer, B. Schölkopf and M. Besserve, Transactions on Machine Learning Research 2022.

Decoding internally generated transitions of conscious contents in the prefrontal cortex without subjective reports, V. Kapoor, A. Dwarakanath, S. Safavi, J. Werner, M. Besserve, T. I. Panagiotaropoulos and N. K. Logothetis. Nature Communications 2022.

From univariate to multivariate coupling between continuous signals and point processes: a mathematical framework, S. Safavi, N. K. Logothetis and M. Besserve. Neural Computation 2021.

Coupling of hippocampal theta and ripples with pontogeniculooccipital waves, J. F. Ramirez-Villegas, M. Besserve, Y. Murayama, H. C. Evrard, A. Oeltermann and N. K. Logothetis. Nature 2020.

Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples, J. F. Ramirez-Villegas, K. F. Willeke, N. K. Logothetis and M. Besserve. Neuron 2018; 100:1016-19.

Parallel and functionally segregated processing of task phase and conscious content in the prefrontal cortex, V. Kapoor, M. Besserve, N.K. Logothetis and F. Panagiotaropoulos. Communications Biology 2018; 1.

Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events, J. F. Ramirez-Villegas, N. K. Logothetis, M. Besserve. Proceedings of the National Academy of Sciences U.S.A 2015; 112:E6379-E6387

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer, M. Besserve, S. C. Lowe, N. K. Logothetis, B. Schölkopf, S. Panzeri. PLOS Biology 2015; 13, e1002257

Metabolic cost as an organizing principle for cooperative learning, D. Balduzzi, P.A. Ortega and M. Besserve. Advances in Complex Systems 2013; 16 :1350012

Multimodal information improves the rapid detection of mental fatigue, F. Laurent , M. Valderrama, M. Besserve, M. Guillard, J.-P. Lachaux, J. Martinerie and G. Florence. Biomedical Signal Processing and Control 2013; 8 :400-8.

Hippocampal-Cortical Interaction during Periods of Subcortical Silence, N. K. Logothetis, O. Eschenko, Y. Murayama, M. Augath, T. Steudel, H. C. Evrard, M. Besserve and & A. Oeltermann. Nature 2012; 491 :547-53.

Extraction of functional information from ongoing brain electrical activity, M. Besserve & J. Martinerie. IRBM 2011; 32 :27-34.

Dynamics of excitable neural networks with heterogeneous connectivity, M. Chavez , M. Besserve & M. Le Van Quyen. Progress in Biophysics and Molecular Biology 2011; 105 :29-33

Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view, M. Besserve, J. Martinerie & L. Garnero, Neuroimage 2011; 55 :1536-1547.

Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis, M. Besserve, B. Schölkopf, N. K. Logothetis and S. Panzeri. Journal of Computational Neuroscience 2010; 29 :547-566.

Source reconstruction and synchrony measurements for revealing functional brain networks and classifying mental states, Laurent F , Besserve M, Garnero L, Philippe M, Florence G and Martinerie J., International Journal of Bifurcation and Chaos 2010; 20 :1703-1721.

Classification methods for ongoing EEG and MEG signals, M. Besserve, K. Jerbi, F. Laurent, S. Baillet, J. Martinerie & L. Garnero. Biological Research 2007; 40 :415-437.

Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities, M. Besserve, M. Phillipe, G. Florence, L. Garnero & J. Martinerie, Clinical Neurophysiology 2008 ; 119 :897-908.

Towards a proper estimation of phase synchronization from time series, M. Chavez, M. Besserve, C. Adam & J. Martinerie, Journal of Neuroscience Methods 2006 ;154 :149-160.

Reports and dissertations

Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machine : un retour aux sources, M. Besserve, PhD dissertation (in french)/Thèse de doctorat. Université Paris-Sud 11 22 Novembre 2007.