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

Program Spring School

Lecture Series

The lecture series will be given by


 

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Alexandre Belloni

Duke University

 

   

 

       

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Sébastien Bubeck

Microsoft Research, Redmond

   

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Vladimir Koltchinskii

Georgia Tech

 
   

 

Schedule

  Mon, Mar 5 Tue, Mar 6 Wed, Mar 7 Thu, Mar 8

 

 

 

Part I

V. Koltchinskii

9:00-10:15

A. Belloni

9:00-10:30

S. Bubeck

9:00-10:15

A. Belloni

9:00-10:00

Break Break Break Break

A. Belloni

10:45-12:00

V. Koltchinskii

11:00-12:30

S. Bubeck

10:45-12:00

S. Bubeck

10:30-11:30

Participants'

Introduction

 

Q&P S. Bubeck

12:00-12:30

V. Koltchinskii

11:30-12:30

End Part I 12:30 12:30 12:30 12:30
Lunch 13:00 13:00 13:00 12:40
 

Break

(Poster preparation)

Break

(Football)

 

 

 

 

 

Excursion

Break

 

 

 

Part II

V. Koltchinskii

15:15-16:30

A. Belloni

15:00-16:15

 

 

 

 

Workshop

15:00

Break Break

Q&P A. Belloni

17:00-17:30

S. Bubeck

16:45-18:00

Q&P V. Koltchinskii

17:30-18:00

 
End Part II 18:00 18:00
Dinner 18:30 18:30 18:30  
 

Poster Session

19:30

FG Meeting    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Lecture notes

Alexandre Belloni: All-in-one

Sébastien Bubeck: All-in-one, Lecture 1, 2, 3, 4

Vladimir Koltchinskii: Slides

Preparatory Material

Alexandre Belloni's course 'Inference with Many Approximate Means and its Applications':

  • Belloni A., Chernozhukov V., Chetverikov D., Wei Y. Uniformly valid post-regularization confidence regions for many functional parameters in z-estimation framework. arXiv:1512.07619, 2015. (forthcoming Annals of Statistics)
  • Chernozhukov, V., Chetverikov, D. and Kato, K. Gaussian approximations and
    multiplier bootstrap for maxima of sums of high-dimensional random vectors. The
    Annals of Statistics 41 2786-2819, 2013.
  • Further related works: 1, 2, 3, 4 & 5

Sébastien Bubeck's course 'Bandit Convex Optimization':

Vladimir Koltchinskii's course 'Asymptotic Efficiency in High-Dimensional Covariance Estimation':

 

Poster session

All participants are encouraged to present their work in a poster session.

 

Participants Short Introduction

All participants introduce themselves and their research interests very briefly on Monday before lunch.