Odalric-Ambrym Maillard - Researcher

Open Positions

Postdoc

Please contact me by email with 3 of your main publications, CV, motivation letter, and recommendation letter.

All positions have been filled for this fall

Ph.D.

Below are listed some funded PhD. Other proposals can be discussed too.

All positions have been filled for this fall

Master internship

Intended for Master 2 or outstanding Master 1 students, generally open the possibility to start a PhD.

Contextual multi-armed bandits for soil-regeneration of agrosystems: Theory and Application, with a use case in Madagascar.

On a broader perspective, I'll be happy to help you achieve your goals in case you

Believe that understanding the dynamics of complex systems, as well as how to optimally act in them can have a huge positive impact on all aspects of human societies that require a careful management of natural, energetic, human and computational resources. That it is thus our duty to optimally answer it.
Consider that for that purpose, due to the limitations of human capabilities to process large amounts of data, we should pursue the long-term development of an optimal and automatic method that can, from mere observations and interactions with a complex system, understand its dynamics and how to optimally act in it.
Want to attack this problem by using any combination of the following four pillar domains: Machine Learning, Mathematical Statistics, Dynamical Systems and Optimization,

Suggested Reading

Books and Manuscripts

Mathematics of Statistical Sequentiel Decision Making, Odalric-ambrym Maillard (my Habilitation dissertation), 2019
Prediction Learning ang Games, Nicolo Cesa-Bianchi, and Gábor Lugosi. Cambridge University Press, 2006.
Concentration inequalities: A nonasymptotic theory of independence, Stéphane Boucheron, Gábor Lugosi, and Pascal Massart. OUP Oxford, 2013.
Self-normalized processes: Limit theory and Statistical Applications, Victor H. Peña, Tze Leung Lai, and Qi-Man Shao. Springer Science & Business Media, 2008.
Pac-Bayesian supervised classification: The thermodynamics of statistical learning, Catoni, Olivier. IMS, 2007.
Bandits algorithms, Tor Lattimore, Csaba Szepesvári.
Algorithms for Reinforcement Learning, Csaba Szepesvári. Synthesis Lectures on Artificial Intelligence and Machine Learning 4.1 (2010): 1-103.
Markov Decision Processes: Discrete Stochastic Dynamic Programming, Martin Puterman.

Lecture notes

Statistical Learning Theory and Sequential Prediction, Alexander Rakhlin, Karthik Sridharan
Concentration of Measure Inequalities in Information Theory, Communications and Coding, Raginsky, Maxim, and Igal Sason. Now Publishers Inc., 2014.
Course on Reinforcement Learning, Alessandro Lazaric.