Talks and lectures by guests and members of HDI Lab (in English)

• Talk by Nikita Puchkin (HSE University) article "Study of the local norm in Exp-concave Statistical Learning" Fall into ML 2023: Conference on Machine Learning (Fall into ML 2023), October 26-28, 2023.

• Talk by Sergey Samsonov (HSE University) article "First-order methods with Markov noise from acceleration to variational inequalities", Fall into ML 2023: Conference on Machine Learning (Fall into ML 2023), October 26-28, 2023.

• Talk by Varvara Rudenko (HSE University) based on the results of joint work with Alexander Gasnikov (NIU) on the article "Algorithm for a limited Markov decision-making process with linear convergence", Fall into ML 2023: Conference on Machine Learning (Fall into ML 2023), October 26-28, 2023.

• Talk by Alexey Naumov (HSE University) on the article "The rapid pace of maximum entropy research", Fall into ML 2023: Conference on Machine Learning (Fall into ML 2023), October 26-28, 2023.

• Talk by Alexey Naumov (HSE University) took part in the panel discussion "Science in the Academy against Industry" (HSE), Fall into ML 2023: Conference on Machine Learning (Fall into ML 2023), October 26-28, 2023.

• Talk by Alexey Naumov (HSE University), Posterior sampling and Bayesian bootstrap: sample complexity and regret bounds (1), Autumn School on Machine Learning (Fall into ML 2022), November's 01-03, 2022.                                                                                                                                                                                                            
• Talk by Daniil Tiapkin (HSE University), Posterior sampling and Bayesian bootstrap: sample complexity and regret bounds (2), Autumn School on Machine Learning (Fall into ML 2022), November's 01-03, 2022.                                                                                                                                                                                                                
  • Talk by Sergey Samsonov (HSE University), Local-Global MCMC kernels: the best of both worlds, Autumn School on Machine Learning (Fall into ML 2022), November's 01-03, 2022.                                                                                                                                                                                                                                                                                 
  • Talk by Dmitrii Ostrovskii (University of Southern California), Near-Optimal Model Discrimination with Non-Disclosure, June 22, 2021.
    
  • Talk "MCMC, Langevin diffusion, and control variates for MCMC" by Eric Moulines (École Polytechnique, HSE University, ENS Paris-Saclay) at the Conference "Theory of Probability and its Applications: P.L. Chebyshev - 200", May 20, 2022

Talk by Quentin Paris (HSE University), Online learning with exponential weights in metric spaces, April 20, 2021

• Talk by Dmytro Perekrestenko (ETH Zurich), Constructive Universal High-Dimensional Distribution Generation through Deep ReLU, March 30, 2021
  
• Talk by Maxim Panov (Skoltech), Stochastic normalizing flows, March 23, 2021

• Talk by Nikita Puchkin (HSE University, IITP), Rates of convergence for density estimation with GANs, February 9, 2021

• Talk by Michel Ledoux (Université de Toulouse, France) "On Optimal Matching of Random Samples", September 17, 2019.
  
•  Mini-course by Eric Moulines (Ecole Polytechnique (Paris), HSE University) “Introduction to reinforcement learning”, April 16 and 23, 2019.
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•  Mini-course by Sergey Bobkov (University of Minnesota, HSE University) “Strong probability distances and limit theorems”, May 17 and 24, 2018.
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•  Talk by Eric Moulines (Ecole Polytechnique (Paris), HSE University) Perturbed Proximal Gradient Algorithms at the Faculty of Computer Science , February 22, 2018.