Webpage of Sebastien Loustau
I am Sebastien Loustau, researcher in the laboratory LMAP at UPPA and also involved in the I-Site E2S-UPPA, initiative d'excellence supported by UPPA, INRIA, INRAE and CNRS (see a french marketing video here).
My research interests are focused on online learning, mathematical statistics, information theory and more generally mathematical statistics for machine learning. I am interested by techniques that process data on the fly, that have mathematical motivations and theoretical guaranteed, such as excess risk or regret bounds. More recently, I am interested in the applications of these activities to deep learning techniques and environmental challenges.
I am also president of the non-profit organization IAPau and the founder of the AI startup LumenAI.
Prior to it, you might have met me in Marseille (Institut de Marseille, previously LATP) where I defended my phd in 2008 under the supervision of the late professor Laurent Cavalier, or in Centrale Marseille or Aix-Marseille 1, where I teached Probability, Statistics and Machine Learning, or more recently in LAREMA, where I defended my Habilitation thesis in 2014 about Online Learning and Inverse Statistical Learning.
Why deep learning ? Because many companies, researchers and engineers, have popularized this family of algorithms 10 years ago thanks to really good performances in computer vision and natural langage processing.
Why environmental challenges ? Because nowadays, there is a scientific evidence about the fact that the unprecedented current warming trend is extremely likely to be the result of human activity since the mid-20th century.
Feel free to send me any emails to discuss my work at sebastien[dot]loustau[at]univ-pau.fr.
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See me on various videos on the web to illustrate my daily work and scientific interests:
- 2020-, Researcher at LMAP of the UPPA,
- 2018-, founder and President of the non-profit organization IAPau,
- 2015-2020, founder and CEO of the french startup LumenAI,
- 2016-2020, founder and organizer of the Pau Machine Learning meetup,
- 2009-2015, assistant professor at Université d'Angers, researcher at LAREMA, UMR-CNRS 6093.
List of selected publications
- Chee, A. and Loustau, S. - Learning with BOT - Bregman and Optimal Transport divergences, 2021 HAL repository,
- Chee, A. and Loustau, S. - Sparsity regret bounds for XNOR-nets++, 2021 HAL repository,
- Li, L., Guedj, B. and Loustau, S. - A quasi-Bayesian perspective to online clustering, Electron. J. Statist., 12(2): 3071–3113. 2018,
- Darmaillac, Y., and Loustau, S. - MCMC Louvain for Online Community Detection, 2017 https://arxiv.org/abs/1612.01489
- Chichignoud, M. and Loustau, S. - Bandwidth selection in kernel empirical risk minimization via the gradient, Ann. Statist., 43(4): 1617-1646. 2015,
- Loustau, S. and Marteau, C. - Minimax fast rates for discriminant analysis with errors in variables, Bernoulli, 21(1): 176-208. 2015,
- Chichignoud, M. and Loustau, S. - Adaptive noisy clustering, IEEE Transactions on Information Theory, 60 (11), 7279-7292. 2014,
- Loustau, S. - Inverse statistical learning, Electronic Journal of Stats, 7: 2065-2097. 2013,
- Loustau, S. - Penalized empirical risk minimization over Besov spaces, Electronic Journal of Stats, 3: 824-850. 2009,
- Loustau, S. - Aggregation of SVM classifiers using Sobolev Spaces, Journal of Machine Learning Research, 9: 1559-1982, 2008.
List of recent talks
- Comment intégrer des contraintes environnementales dans l'apprentissage profond ?, PFIA'21, track Nouvelle-Aquitaine, invitation Nicolas Roussel, juillet 2021 program here,
- IA et réchauffement climatique, Conférence de vulgarisation au Lycée Louis Barthou, mai 2021 vidéo ici et un article ici.
- Power efficient Deep Learning, AI4Climate workshop, invitation Julien Brajard , oct 2020 download slides here,
My phd is also available here and my habilitation thesis here.