Conference program

FRIDAY, APRIL 28, 2017

10:00 – 10:20
Registration Opens
10:20 – 10:30
Arkady Borkovsky
Russia, Yandex
Welcome & Keynote Address
Prof. Ilya Muchnik
USA, Rutgers University (NJ)

Session I. Learning Theory

Chair Prof. Vladimir Vovk
UK, Royal Holloway, University of London
10:30 – 11:15
Prof. Evgeny Bauman
USA, NJ, Markov Processes International
Dr. Konstantin Bauman
USA, Stern School of Business New York University
Detecting Linearly Separable Class by its Mean
11:15 – 12:00
Prof. Vadim Mottl
Russia, Computng Center of the Russian Academy of Sciences,
Dr. Oleg Seredin
Russia, Tula State University
Compactness hypothesis and potential functions in Machine Learning
 
12:00 – 12:15
Break
 
12:15 – 13:00
Dr. Leon Bottou
USA, Facebook
Beyond Statistical Machine Learning
 
13:00 – 14:15
Lunch
 
14:15 – 15:00
Prof. Vladimir Vapnik
USA, ColumbiaUniversity
50 years for Machine Learning
15:00 – 16:00
Prof. Lev Rozonoer
USA, MA, West Newton
About composition complexity
 
16:00 – 16:15
Break
 
16:15 – 17:00
Prof. Alexander Gammerman
UK, Royal Holloway, University of London
Conformal prediction and testing
17:00 – 17:45
Prof. Vladimir Vovk
UK, Royal Holloway, University of London
Conformal predictive distributions
17:45 – 18:30
Prof. Evgeny Burnaev and
Dr. Ivan Nazarov
Russia, Moscow, Skoltech, IITP
Conformalized Kernel Ridge Regression

SATURDAY, APRIL 29, 2017

Session II. Kernel Functions and Clustering

Chair Prof. Alexander Gammerman
UK, Royal Holloway, University of London
10:00 – 10:45
Dr. Valentina Sulimova
Russia, Tula, Tula State University
Potential functions for signals and sequences
10:45 – 11:30
Prof. Boris Mirkin
Russia, Moscow, National Research University Higher School of Economics, Moscow, Russia, and UK, Birkbeck University of London
Braverman's Spectrum, Matrix Diagonalization, and K-Means: A Unified Framework for Clustering
 
11:30 – 11:45
Break
 
11:45 – 12:30
Prof. Jennifer Dy
USA, Boston, Northeastern University
Learning Multiple Alternative Clustering Views
12:30 – 13:15
Dr. Rodrigo Franco Toso
USA, Microsoft
Variance-aware clustering as an alternative to k-means and two applications in finance
 
13:15 – 14:30
Lunch
 

Session III. Applications

Chair Prof. Evgeny Burnaev
Russia, Moscow, Skoltech, IIT
14:30 – 15:15
Prof. Pierre Baldi
USA, University of California in Irvine
Deep Learning Applications in the Natural Sciences
15:15 – 16:00
Prof. Vladimir Lumelsky
USA, Wisconsin, University of Wisconsin-Madison
Human-Robot Teams: Interaction Between Mathematical and Hardware Issues
 
16:00 – 16:15
Break
 
16:15 – 17:00
Prof. Pierre Baldi
USA, University of California in Irvine
Deep Learning in the Machine
17:00 – 17:45
Dr. Igor Mandel
USA, New York, Telmar Inc.
Troublesome causal modeling and statistics
 

SUNDAY, APRIL 30, 2017

Session IV. New Directions

Chair Prof. Vadim Mottl
Russia, Computing Center of the Russian Academy of Sciences
10:00 – 10:45
Prof. Ilya Muchnik
USA, NJ, Rutgers University, CS Department and Russia, Moscow, Yandex School of Data Analysis
Personalized treatment in medicine based on feature grouping
10:45 – 11:30
Prof. Semyon Meerkov
USA, University of Michigan
Braverman’s Contributions to Justification of Describing Functions Method, Their Extensions, and Open Problems
 
11:30 – 11:45
Break
 
11:45 – 12:30
Prof. Mark Levin
National Research University Higher School of Economics, Moscow, Russia
From Walrasian Equilibrium to Braverman's Disequilibrium
 
12:30 – 13:15
Lunch
 

Session V. About Emmanuel Braverman

Chair Semyon Meerkov
USA, University of Michigan
13:15 – 14:00
Prof. Lev Rozonoer
USA, MA, West Newton
Emmanuel Braverman as a human without restrictions
14:00 – 14:45
Prof. Boris Mirkin
National Research University Higher School of Economics, Moscow, Russia, and Birkbeck University of London, UK
Misha Braverman: my mentor and my model
 
14:45 – 15:00
Break
 
15:00 – 15:45
Prof. Mark Levin
National Research University Higher School of Economics, Moscow, Russia
Eight years with Emannuel Braverman
15:45
Arkady Borkovsky
Russia, Yandex
Closing Remarks