Odalric-Ambrym Maillard

Postdoc

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

• PEPR Pl@ntAgroEco, 18 months, Spring 2024, contextual bandits for image annotation. Apply here.

Ph.D.

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

• ANR BIP-UP project, contextual multi-armed bandits for patient follow-up. Apply here

Master internship

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

• Bandits exploiting contextual structure in the non-parametric, Huber-outlier setup
• Contextual multi-armed bandits for soil-regeneration of agrosystems: Theory and Application, with a use case in Madagascar.
• Contextual multi-armed bandits for patient follow-up, with a use case at Lille Hospital.

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.