Internships

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.