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ORCIDMichel Besserve
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2020 – today
- 2024
[i23]Patrick Burauel, Frederick Eberhardt, Michel Besserve:
Controlling for discrete unmeasured confounding in nonlinear causal models. CoRR abs/2408.05647 (2024)- 2023
- [j8]Shervin Safavi
, Theofanis I. Panagiotaropoulos, Vishal Kapoor, Juan F. Ramirez-Villegas, Nikos K. Logothetis, Michel Besserve:
Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Comput. Biol. 19(4) (2023) - [c20]Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf:
Structure by Architecture: Structured Representations without Regularization. ICLR 2023 - [c19]Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf:
Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. ICML 2023: 16190-16215 - [c18]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS 2023 - [c17]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. NeurIPS 2023 - [i22]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. CoRR abs/2305.17225 (2023) - [i21]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR abs/2306.00542 (2023) - [i20]Armin Kekic, Bernhard Schölkopf, Michel Besserve:
Targeted Reduction of Causal Models. CoRR abs/2311.18639 (2023) - [i19]Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf:
Independent Mechanism Analysis and the Manifold Hypothesis. CoRR abs/2312.13438 (2023) - 2022
- [j7]Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve:
Causal Feature Selection via Orthogonal Search. Trans. Mach. Learn. Res. 2022 (2022) - [c16]Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf:
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. CLeaR 2022: 110-143 - [c15]Simon Buchholz, Michel Besserve, Bernhard Schölkopf:
Function Classes for Identifiable Nonlinear Independent Component Analysis. NeurIPS 2022 - [c14]Felix Leeb, Stefan Bauer, Michel Besserve, Bernhard Schölkopf:
Exploring the Latent Space of Autoencoders with Interventional Assays. NeurIPS 2022 - [c13]Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve:
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. NeurIPS 2022 - [c12]Michel Besserve, Bernhard Schölkopf:
Learning soft interventions in complex equilibrium systems. UAI 2022: 170-180 - [d1]Patrik Reizinger, Luigi Gresele, Jack Brady, Dominik Zietlow, Julius von Kügelgen, Michel Besserve, Georg Martius, Wieland Brendel, Bernhard Schölkopf:
ima-vae. Zenodo, 2022
- [i18]Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf:
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective". CoRR abs/2202.06844 (2022) - [i17]Kaidi Shao, Nikos K. Logothetis, Michel Besserve:
Bayesian Information Criterion for Event-based Multi-trial Ensemble data. CoRR abs/2204.14096 (2022) - [i16]Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve:
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. CoRR abs/2206.02416 (2022) - [i15]Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf:
Homomorphism Autoencoder - Learning Group Structured Representations from Observed Transitions. CoRR abs/2207.12067 (2022) - [i14]Simon Buchholz, Michel Besserve, Bernhard Schölkopf:
Function Classes for Identifiable Nonlinear Independent Component Analysis. CoRR abs/2208.06406 (2022) - 2021
- [j6]Shervin Safavi, Nikos K. Logothetis, Michel Besserve:
From Univariate to Multivariate Coupling Between Continuous Signals and Point Processes: A Mathematical Framework. Neural Comput. 33(7): 1751-1817 (2021) - [c11]Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf:
A Theory of Independent Mechanisms for Extrapolation in Generative Models. AAAI 2021: 6741-6749 - [c10]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS 2021: 16451-16467 - [c9]Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve:
Independent mechanism analysis, a new concept? NeurIPS 2021: 28233-28248 - [i13]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR abs/2106.04619 (2021) - [i12]Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve:
Independent mechanism analysis, a new concept? CoRR abs/2106.05200 (2021) - [i11]Michel Besserve, Bernhard Schölkopf:
Learning soft interventions in complex equilibrium systems. CoRR abs/2112.05729 (2021) - 2020
- [c8]Michel Besserve, Arash Mehrjou, Rémy Sun, Bernhard Schölkopf:
Counterfactuals uncover the modular structure of deep generative models. ICLR 2020 - [i10]Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf:
A theory of independent mechanisms for extrapolation in generative models. CoRR abs/2004.00184 (2020) - [i9]Anant Raj, Stefan Bauer, Ashkan Soleymani, Michel Besserve, Bernhard Schölkopf:
Causal Feature Selection via Orthogonal Search. CoRR abs/2007.02938 (2020) - [i8]Daniel Chicharro, Michel Besserve, Stefano Panzeri:
Causal learning with sufficient statistics: an information bottleneck approach. CoRR abs/2010.05375 (2020)
2010 – 2019
- 2019
- [c7]Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schölkopf:
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. UAI 2019: 207-216 - 2018
- [c6]Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. AISTATS 2018: 557-565 - [i7]Philipp Geiger, Justus Winkelmann, Claudius Proissl, Michel Besserve, Bernhard Schölkopf:
Coordination via predictive assistants from a game-theoretic view. CoRR abs/1803.06247 (2018) - [i6]Michel Besserve, Rémy Sun, Bernhard Schölkopf:
Counterfactuals uncover the modular structure of deep generative models. CoRR abs/1812.03253 (2018) - 2017
- [i5]Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. CoRR abs/1705.02212 (2017) - 2015
- [c5]Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve:
Telling cause from effect in deterministic linear dynamical systems. ICML 2015: 285-294 - [i4]Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve:
Telling cause from effect in deterministic linear dynamical systems. CoRR abs/1503.01299 (2015) - [i3]Kun Zhang, Biwei Huang, Bernhard Schölkopf, Michel Besserve, Masataka Watanabe, Dajiang Zhu:
Towards Robust and Specific Causal Discovery from fMRI. CoRR abs/1509.08056 (2015) - 2013
- [j5]David Balduzzi, Pedro A. Ortega, Michel Besserve:
Metabolic Cost as an Organizing Principle for Cooperative Learning. Adv. Complex Syst. 16(2-3) (2013) - [j4]François Laurent, Mario Valderrama, Michel Besserve, Mathias Guillard, Jean-Philippe Lachaux, Jacques Martinerie, Geneviève Florence:
Multimodal information improves the rapid detection of mental fatigue. Biomed. Signal Process. Control. 8(4): 400-408 (2013) - [c4]Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf:
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. NIPS 2013: 2535-2543 - 2012
- [c3]David Balduzzi, Michel Besserve:
Towards a learning-theoretic analysis of spike-timing dependent plasticity. NIPS 2012: 2465-2473 - [i2]David Balduzzi, Pedro A. Ortega, Michel Besserve:
Metabolic cost as an organizing principle for cooperative learning. CoRR abs/1202.4482 (2012) - [i1]David Balduzzi, Michel Besserve:
Towards a learning-theoretic analysis of spike-timing dependent plasticity. CoRR abs/1209.5549 (2012) - 2011
- [j3]Michel Besserve, Jacques Martinerie, Line Garnero:
Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view. NeuroImage 55(4): 1536-1547 (2011) - [c2]Michel Besserve, Dominik Janzing, Nikos K. Logothetis, Bernhard Schölkopf:
Finding dependencies between frequencies with the kernel cross-spectral density. ICASSP 2011: 2080-2083 - 2010
- [j2]François Laurent, Michel Besserve, Line Garnero, Matthieu Philippe, Geneviève Florence, Jacques Martinerie:
Source Reconstruction and Synchrony Measurements for Revealing Functional Brain Networks and Classifying Mental States. Int. J. Bifurc. Chaos 20(6): 1703-1721 (2010) - [j1]Michel Besserve, Bernhard Schölkopf, Nikos K. Logothetis, Stefano Panzeri:
Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis. J. Comput. Neurosci. 29(3): 547-566 (2010)
2000 – 2009
- 2008
- [c1]Michel Besserve, Jacques Martinerie, Line Garnero:
Non-invasive classification of cortical activities for brain computer interface: A variable selection approach. ISBI 2008: 1063-1066 - 2007
- [b1]Michel Besserve:
Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machines : un retour aux sources. University of Paris-Sud, Orsay, France, 2007
Coauthor Index
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