Key topics of the conference:

  • 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.)


We invite authors of A* and Q1 papers (2021-2022) to present their work. Each talk will be 15 min long. 

Programme 3 November

Session 1&2
10:00 - 11:15
Session 1: Applied ML 1 (Big Hall)
Speakers: Anton Chernyavskiy & Dmitry Ilvovsky Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks
Alexander Panchenko ParaDetox: Detoxification with Parallel Data
Alexander Chernyavskiy Improving Text Generation via Neural Discourse Planning
Sergey Kuznetsov Δ-Closure Structure for Studying Data Distribution
Nikita Pospelov Ownership concentration and wealth inequality in Russia
Session 2: Optimization (Small Hall)
Speakers: Anton Novitskii The power of first-order smooth optimization for black-box non-smooth problems
Darina Dvinskikh Improved complexity bounds in wasserstein barycenter problem
Aleksandr Beznosikov Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Ekaterina Borodich Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity
Dmitry Yarotski A new analytic approach to SGD with momentum and its applications: phase transitions and benefit from negative momenta
11:15 - 11:45 Coffee break
Session 3&4
11:45 - 13:00
Session 3: Applied ML 2 (Small Hall)
Speakers: Ruslan Rakhimov NPBG++: Accelerating Neural Point-Based Graphics
Denis Kuznedelev oViT: A Sparsification Framework for Vision Transformers
Andrey Savchenko & Liudmila Savchenko Video-based facial expression recognition and engagement prediction for mobile devices
Alexander Gushchin & Maksim Smirnov & Sergey Lavrushkin Video compression dataset and benchmark of learning-based video-quality metrics
Ivan Rubachev On Embeddings for Numerical Features in Tabular Deep Learning
Session 4: Computational ML (Big Hall)
Speakers: Andrei Chertkov & Konstantin Sozykin TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
Daniil Tiapkin Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Sergey Samsonov Local-Global MCMC kernels: the best of both worlds
Maxim Rakhuba & Alexandra Senderovich Towards Practical Computation of Singular Values of Convolutional Layers
13:00 - 14:00 Free time
Session 5&6
14:00 - 15:15
Session 5: Theoretical ML  (Big Hall)
Speakers: Nazar Buzun Strong Gaussian Approximation for the Sum of Random Vectors 
Nikita Puchkin Exponential savings in agnostic active learning through abstention
Maxim Kodryan Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
Alexey Kornaev Physics-based loss and machine learning approach in application to non-Newtonian fluids flow modeling
Alexey Naumov Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation
Session 6: Generative modeling and representation learing (Small Hall)
Speakers: Fedor Noskov Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Aybek Alanov HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Alexander Korotin Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
Mikhail Pautov Smoothed Embeddings for Certified Few-Shot Learning
15:15 - 15:45 Coffee 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

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