Conveners
Talks: Bayesian inference vs stochastic optimization
- Vladimir Spokoiny (WIAS, IITP, HSE)
Talks: Wasserstein-2 Generative Networks
- Evgeny Burnaev (Skoltech)
Talks: Towards Photorealistic Neural Avatars
- Viktor Lempitsky (Samsung AI, Skoltech)
Talks: Approximation of multivariate functions using deep learning with applications
- Ivan Oseledets (Skoltech)
Talks: Differentiating the Black-Box: Optimization with Local Generative Surrogates
- Vladislav Belavin (HSE University)
Talks: Structure-adaptive manifold estimation
- Nikita Puchkin (HSE)
Talks: Fast Simulation Using Generative Adversarial Networks in LHCb
- Artem Maevskiy (HSE University)
Talks: Uncertainty estimation: can your neural network provide confidence for its predictions?
- Maxim Panov (Skoltech, HSE)
Talks: Adaptive Divergence for Rapid Adversarial Optimization
- Maxim Borisyak (Yandex School of Data Analysis (RU))
Talks: (1+ε)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
- Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
Talks: New directions in generative models and high-dimensional MC methods
- Eric Moulines (Ecole Polytechnique, HSE University)
