We have one open postdoc position, under super-vision by OA Maillard. The topic is on adressing novel challenges in Reinforcement Learning theory. These challenges are inspired by an application of sequential decision making to biodiversity-preserving agriculture. Please contact O-A. Maillard with 3 of your main publications, CV, motivation letter and recommendation letter.
In case you want to apply for PhD, I strongly encourage you to read (a substantial part of) the following books and lecture notes:
- 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
- Mathematics of Statistical Sequentiel Decision Making.
Odalric-ambrym Maillard (my Habilitation dissertation).
- 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
In the context of a new project called « Sequential Recommendation for Sustainable Gardening », we are looking for a talented software developer experienced with back-end/front-end development, crowdsourcing platforms/recommender systems, to participate in building an ambitious research platform for sharing of good agricultural practice. Job description, see also Official Job Application.
We have a list of (funded) internships proposals, please contact me in case you are interested. We expect students with a solid mathematical background specifically in statistics, information theory and/or dynamical systems.
- Prediction with confident expert advice.
- Upper Confidence Reinforcement Learning with abstraction of states.
- Self-regression, with application to learning state-representation in MDPs.
- PAC renewal theory, turnpike and blackwell optimality in MDPs.
These are intended for Master 2 or outstanding Master 1 students, and generally open the possibility to start a PhD later on. If you are interested, go ahead and contact me directly.