Publications
| Preprints |
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Long time analysis of a pair of on-lattice and continuous run-and-tumble particles with hard-core interactions A. Guillin, L. Hahn, and M. Michel arXiv 2411.13964 |
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Central
Limit Theorem for Bayesian Neural Networks trained with Variational Inference A. Descours, T. Huix, A. Guillin, M. Michel, É. Moulines, and B. Nectoux arXiv 2406.09048 |
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Jamming pair of general run-and-tumble particles: Exact results and universality classes L. Hahn, A. Guillin, and M. Michel arXiv 2306.00831 |
Published |
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Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity V. Souveton, A. Guillin, J. Jasche, G. Lavaux and M. Michel Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) (2024) |
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Necessary and sufficient symmetries in Event-Chain Monte Carlo with generalized flows and application to hard dimers T. Guyon, A. Guillin, and M. Michel The Journal of Chemical Physics 160 (2), 024117 (2024) Editor's Pick and 2023 JCP Emerging Investigators Special Collection |
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Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference A. Descours, T. Huix, A. Guillin, M. Michel, É. Moulines, and B. Nectoux Proceedings of Thirty Sixth Conference on Learning Theory (COLT) (2023) |
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Law of large numbers and central limit theorem for wide two-layer neural networks: the mini-batch and noisy case A. Descours, A. Guillin, M. Michel and B. Nectoux Journal of Machine Learning Research 25 (208), 1–76 (2024). |
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PDMP characterisation of event-chain Monte Carlo algorithms for particle systems A. Monemvassitis, A. Guillin and M. Michel, Journal of Statistical Physics 190, 66 (2023). |
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Loop-Cluster Monte Carlo Algorithm for Classical Statistical Models L. Zhang, M. Michel, E. M. Elçi and Y. Deng, Physical Review Letters 125 (20), 200603 (2020). |
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Forward
Event-chain Monte Carlo: : Fast Sampling by Randomness Control in Irreversible Markov Chains M. Michel, A. Durmus and S. Sénécal, JCGS, 29 (4), 689-702 (2020). |
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Clock Monte Carlo
methods M. Michel, X. Tan and Y. Deng, PRE, 99, 010105, Rapid Communication (2019). |
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Event-chain Monte Carlo algorithms for three- and many-particle
interactions J. Harland, M. Michel, T. A. Kampmann and J. Kierfeld, EPL, 117, 30001 (2017) - editor's choice. |
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Event-chain
algorithm for the Heisenberg model: Evidence for z ~ 1
dynamic scaling Nishikawa Y., Michel M., Krauth W., Hukushima K., Physical Review E, 92, 063306 (2015). |
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Event-chain
Monte Carlo for classical continuous spin models Michel M., Mayer J., Krauth W., EPL, 112, 20003 (2015). |
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Generalized
event-chain Monte Carlo: Constructing rejection-free
global-balance algorithms from infinitesimal steps Michel M., Kapfer S., Krauth W., Journal of Chemical Physics, 140, 054116 (2014). |
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Mass distributions of stars and cores in young groups and
clusters Michel M., Kirk H., Myers P., Astrophysical Journal, 735, 51 (2011). |
| Thesis |
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Analytical and computational developments around long-time behaviors
of stochastic processes M. Michel, HDR thesis defended on June 28th, 2024 at UCA, Aubière, France. |
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Irreversible Markov chains by the factorized Metropolis
filter: Algorithms and applications in particle systems
and spin models M. Michel, PhD thesis defended on October 17th, 2016 at ENS, Paris, France. |