Conference
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.)
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 | ||
