Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Research Unit 1735


The program consists of three lecture series (4.5 hours each) plus associated exercise sessions, a poster session, and short-talks given by the participants.





The lectures (including exercise sessions) will be given by


  • Martin Wainwright (UC Berkeley)

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    Non-parametric estimation: Non-asymptotic guarantees and high-dimensional scaling
  • Sasha Rakhlin (University of Pennsylvania)

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    From Statistical to Online Learning
    We will start with the problems of statistical learning and
    estimation, and show that empirical processes play a key role in
    analyzing the minimax performance. We will then make a natural
    transition to the online learning world. After building a toolbox of
    "sequential" analogues of empirical process theory, we will analyze
    online regression and other online learning problems. A relaxation
    framework, based on approximate dynamic programming, will provide a
    general approach to developing computationally attractive prediction
    methods. Many examples will be discussed, along with future
  • Judith Rousseau (Université Paris-Dauphine)

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    Lectures on non-parametric Bayes

    Some of the topics to be covered are:

    • posterior consistency in Bayesian large dimensional models
    • empirical Bayes
    • posterior concentration rates: Bernstein von Mises
    • frequentist coverage properties of Bayesian credible regions in large or infinite dimensional models


Poster Session

There will be a poster session. All participants are strongly encouraged to contribute a poster (vertical A0 format preferred, but you may deviate from this).


Participants Short Introduction

All participants should give a mini presentation in order to briefly introduce themselves and their research interests within 2-3 minutes, supported by one or at most two slides. Please send your slide(s) until March 11 as a single .pdf-file to