Here is a list of (funded) interships proposals for the year 2016-2017.

- Prediction with
*confident*expert advice. - High-Dimension Online Statistical Decision Making.
- Further optimal bandit strategies.
- 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. If you are interested, go ahead and contact me directly.

I also propose PhD topics. The best way for this is to discuss with me. Here are a few examples:

**Sequential prediction of confidence sets for non-stationary signals:**

This is a highly theoretical topic, however with huge applicative potential. This topic requires a strong candidate trained in Mathematical Statistics, with focus on Model selection, Information Theory, Concentration inequalities, and Signal Processing. A descent knowledge of a programming language such as Python and some basic machine learning library is a plus.**Robust max-flow learning for computational sustainability:**

This topic is fairly balanced between theory and practice, and is opening a new application domain with huge societal interest. It requires a strong candidate trained in Reinforcement Learning theory and/or Control theory, proficient in Graph theory and having excellent programming skills. Good knowledge of statistical learning is also assumed.

In case you want to apply for a PhD, I strongly encourage you to read (a substantial part of) the following books and lecture notes:

Books

- 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.

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.

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