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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 three-day workshop will consist of 4 interdisciplinary mini-courses, a series of short talks (20 min long) and a poster session. The third day of the school will be very similar to an A* level conference: short papers and a long poster session. 

Key topics of this school:

  • General Machine Learning
  • Deep Learning (architectures, generative models, deep reinforcement learning, etc.)
  • Reinforcement learning
  • NLP
  • Learning Theory (bandits, game theory, statistical learning theory, etc.)
  • Optimization (convex and non-convex optimization, matrix/tensor methods, etc.)
  • Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)
  • Trustworthy Machine Learning (accountability, causality, fairness, privacy, robustness, etc.)
  • Applications (computational biology, crowdsourcing, healthcare, neuroscience, social good, climate science, etc.)

Mini-courses

4 speakers (TBA)

1. Posterior sampling and Bayesian bootstrap: sample complexity and regret bounds. Alexey Naumov (HSE)

2. Optimal Transport and Generative Modeling based on Stochastic Processes. Evgeny Burnaev (Skoltech)

Talks

We invite authors of A* papers (2021-2022) to present their work. Each talk will be 20 min long. 

 

Tentative list of speakers

TBA

Evgeny V. Burnaev

Skoltech

Alexey Naumov

HSE University

Max Ryabinin

HSE University

Sergey Samsonov

HSE University

Nikita Puchkin

HSE University

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)

Poster Session

We invite authors of A* papers (2021-2022) to present their work

Poster Submission Guidelines: Only posters of submitted and accepted abstracts will be given presentation space. Please note the following information for the preparation of your poster. Please bring your printed poster with you to the School.

Poster Preparation

Programme

  • Nov 1

    09:30 - 10:00 Registration and welcome

    10:00 - 13:00 Course 1 (with coffee break)

    13:00 - 14:30 Lunch

    14:30 - 17:30 Course 2 (with coffee break)

    17:30 - 18:00 Discussion

    Nov 2

    10:00 - 13:00 Course 3 (with coffee break)

    13:00 - 14:30 Lunch

    14:30 - 17:30 Course 4 (with coffee break)

    17:30 - 18:00 Discussion

    Nov 3

    10:00 - 13:00 Talks ( 2 parallel sessions with coffee break) 

    13:00 - 14:30 Lunch

    14:30 - 16:00 Talks ( 2 parallel sessions with coffee break) 

    16:00 - 18:00 Poster session + welcome

    18:00 - School Dinner

Register