Here is a list of (funded) internships proposals for the year 2016-2017:
- Prediction with confident expert advice.
- Further optimal bandit strategies.
- High-Dimension Online Statistical Decision Making.
- Upper Confidence Reinforcement Learning with abstraction of states.
These are intended for Master 2 or outstanding Master 1 students, and open the possibility to start a PhD in September 2017 that is fully funded by the ANR program BADASS. If you are interested, go ahead and contact me directly. Here are further details regarding the PhD topic.
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.
- Pac-Bayesian supervised classification: The thermodynamics of statistical learning
Catoni, Olivier. IMS, 2007.
- 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.
- Algorithms for Reinforcement Learning
Csaba Szepesvári. Synthesis Lectures on Artificial Intelligence and Machine Learning 4.1 (2010): 1-103.
- 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