Daniil Tiapkin
- Junior Research Fellow, Doctoral Student: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 Programme (Group of Young Academic Professionals)
Category "New Researchers" (2022-2023)
Postgraduate Studies
1st year of study
Approved topic of thesis: Randomized Exploration for Reinforcement Learning
Academic Supervisor: Naumov, Alexey
Courses (2022/2023)
- Fundamentals of Matrix Computations (Bachelor’s programme; Faculty of Computer Science; 2 year, 3, 4 module)Rus
- Mathematical Statistics (advanced course) (Bachelor’s programme; Faculty of Computer Science; 2 year, 3, 4 module)Rus
- Past Courses
Courses (2021/2022)
Algorithms and Data Structures (Bachelor’s programme; Faculty of Computer Science; 1 year, 2, 4 module)Rus
Publications13
- Chapter Tiapkin D., Belomestny D., Calandriello D., Moulines E., Munos R., Naumov A., Perrault P., Tang Y., Valko M., Menard P. Fast Rates for Maximum Entropy Exploration, in: Proceedings of the 40th International Conference on Machine Learning: Volume 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USA Vol. 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USA. PMLR, 2023. P. 34161-34221.
- Chapter Tiapkin D., Belomestny D., Calandriello D., Moulines E., Munos R., Naumov A., Perrault P., Valko M., Menard P. Model-free Posterior Sampling via Learning Rate Randomization, in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc., 2023. P. 73719-73774.
- Chapter Schechtman S., Tiapkin D., Muehlebach M., Moulines E. Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold, in: Proceedings of Machine Learning Research: Volume 195: The Thirty Sixth Annual Conference on Learning Theory, 12-15 July 2023, Bangalore, India Vol. 195: The Thirty Sixth Annual Conference on Learning Theory, 12-15 July 2023, Bangalore, India. PMLR, 2023. P. 1228-1258.
- 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 Тяпкин Д. Н., Шабанов Д. А. О структуре множества полноцветных раскрасок случайного гиперграфа // Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика). 2023. Т. 512. № 1. С. 52-57. doi
- 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
- Article Масютин А. А., Савченко А. В., Наумов А. А., Самсонов С. В., Тяпкин Д. Н., Беломестный Д. В., Морозова Д. С., Бадьина Д. А. О разработке прикладных решений на основе искусственного интеллекта для обеспечения технологической безопасности // Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика). 2022. Т. 508. № 106. С. 23-27. 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.
‘Every Article on NeurIPS Is Considered a Significant Result’
Staff members of the HSE Faculty of Computer Science will present 12 of their works at the 37th Conference and Workshop on Neural Information Processing Systems (NeurIPS), one of the most significant events in the field of artificial intelligence and machine learning. This year it will be held on December 10–16 in New Orleans (USA).
17 Articles by Researchers of HSE Faculty of Computer Science Accepted at NeurIPS
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).