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Regular version of the site

School 1-2 November

HSE University invites you to join an autumn school on machine learning aimed at undergraduate/graduate students and young postdoctoral fellows from pure and applied mathematics. This intense two-day event will consist of 4 interdisciplinary mini-courses.

Conference 3 November

The third day is conference. It will be very similar to an A* level conference: short papers (15 min), long poster session. 

Tentative list of speakers

Evgeny Burnaev

Skoltech, AIRI

Alexey Naumov

HSE University

Ivan Oseledets

Skoltech, AIRI

Dmitry Vetrov

HSE University, AIRI

Aybek Alanov

HSE University, AIRI

Anton Chernyavskiy

HSE University

Darina Dvinskikh

HSE University

Dmitry Ilvovsky

HSE University

Maxim Kodryan

HSE University

Alexander Kolesov

Skoltech

Alexey Kornaev

Innopolis University

Alexander Korotin

Skoltech, AIRI

Sergei Kuznetsov

HSE University

Dmitry Molchanov

HSE University

Anton Novitsky

MIPT

Fyodor Noskov

HSE University, Skoltech

Nikita Puchkin

HSE University

Maxim Rakhuba

HSE University

Ivan Rubachev

Yandex & HSE

Andrey Savchenko

HSE University

Liudmila Savchenko

HSE University

Sergey Samsonov

HSE University

Alexandra Senderovich

HSE University

Maksim Smirnov

Lomonosov MSU

Daniil Tiapkin

HSE University

This school is supported by the RSF grant N19-71-30020 "Applications of probabilistic artificial neural generative models in the development of digital twin technology for Non-linear stochastic systems" and jointly organized by three laboratories of HSE University:

  • HDI Lab (International laboratory of stochastic algorithms and high-dimensional inference)
  • LAMBDA (Laboratory of Methods for Big Data Analysis)
  • DeepBayes (Centre of Deep Learning and Bayesian Methods)

Programme

  • Nov 1


    School

    09:30 - 10:00 Registration and welcome

    10:00 - 11:15 Posterior sampling and Bayesian bootstrap. Part 1

    11:15 - 11:45 Coffee break 

    11:45 - 13:00 Posterior sampling and Bayesian bootstrap. Part 2

    13:00 - 14:00 Free time

    14:00 - 15:15 Tensors and machine learning. Part 1

    15:15 - 15:35 Сoffee break

    15:35 - 16:50 Tensors and machine learning. Part 2

    17:00 - 17: 30 Local-Global MCMC kernels: the best of both worlds

    17:30 - 18:00   Computer vision on mobile devices

    Nov 2


    School

    10:00 - 11:15 Optimal Transport and Generative Modeling based on Stochastic Processes. Part 1

    11:15 - 11:45 Coffee break 

    11:45 - 13:00 Optimal Transport and Generative Modeling based on Stochastic Processes. Part 2

    13:00 - 14:00 Free time

    14:00 - 15:15 Introduction to diffusion models. Part 1

    15:15 - 15:35 Coffee break 

    15:35 - 16:50 Introduction to diffusion models. Part 2

    17:00 - 17:30 Machine learning and astrophysics

    17:30 - 18:00 Machine Learning Challenges in Particle Physics

    Nov 3


    Conference

    10:00 - 11:15 Session 1: Applied ML & Session 2: Optimization

    11:15 - 11:45 Сoffee break

    11:45  - 13:00 Session 3: Applied ML 2 & Session 4: Computational ML

    13:00 - 14:00 Free time

    14:00 - 15:15 Session 5: Theoretical ML & Session 6: Generative modeling and representation learing

    15:15 - 15:45 Сoffee break

    15:45 - 16:00 Conference Photo

    16:00 - 16:05 MML Olympiad award ceremony

    16:05 - 17:30 Poster session 

    17:30 - 19:30 Conference Dinner

Video

Register