- Junior Research Fellow:Faculty of Computer Science / AI and Digital Science Institute / International Laboratory of Stochastic Algorithms and High-Dimensional Inference
- Daniil Tiapkin has been at HSE University since 2019.
Young Faculty Support Program (Group of Young Academic Professionals)
Category "New Researchers" (2022-2023)
- Article Tiapkin D., Belomestny D., Naumov A., Valko M., Menard P. Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms // Working papers by Cornell University. Series math "arxiv.org". 2023. Article 2304.03056.
- Article Schechtman S., Tiapkin D., Moulines E., Jordan M. I., Muehlebach M. First-Order Constrained Optimization: Non-smooth Dynamical System Viewpoint // IFAC-PapersOnLine. 2022. Vol. 55. No. 16. P. 236-241. doi
- Chapter Tiapkin D., Belomestny D., Moulines E., Naumov A., Samsonov S., Tang Y., Valko M., Menard P. From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses, in: Proceedings of the 39th International Conference on Machine Learning Vol. 162. PMLR, 2022. P. 21380-21431.
- Chapter Tiapkin D., Belomestny D., Calandriello D., Éric Moulines, Munos R., Naumov A., Rowland M., Valko M., Menard P. Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees, in: Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022. Curran Associates, Inc., 2022. P. 10737-10751.
- Chapter Tiapkin D., Alexander Gasnikov. Primal-Dual Stochastic Mirror Descent for MDPs, in: International Conference on Artificial Intelligence and Statistics, 28-30 March 2022, A Virtual Conference Vol. 151: Proceedings of The 25th International Conference on Artificial Intelligence and Statistics. PMLR, 2022. P. 9723-9740.
- Article Tiapkin D., Gasnikov A., Dvurechensky P. Stochastic saddle-point optimization for the Wasserstein barycenter problem // Optimization Letters. 2022. Vol. 16. No. 7. P. 2145-2175. doi
- Chapter Dvinskikh D., Tiapkin D. Improved Complexity Bounds in Wasserstein Barycenter Problem, in: International Conference on Artificial Intelligence and Statistics, 13-15 April 2021, Virtual Vol. 130. PMLR, 2021. P. 1738-1746.
- Chapter Dvinskikh D., Tiapkin D. Improved Complexity Bounds in Wasserstein Barycenter Problem, in: Proceedings of Machine Learning Research Volume 130: International Conference on Artificial Intelligence and Statistics. , 2021. P. 1738-1746.
In 2022, 17 articles by the researchers of HSE Faculty of Computer Science were accepted at the NeurIPS (Conference and Workshop on Neural Information Processing Systems), one of the world’s most prestigious events in the field of machine learning and artificial intelligence. The 36th conference will be held in a hybrid format from November 28th to December 9th in New Orleans (USA).