Daniil Tiapkin

According to our database, Daniil Tiapkin authored at least 13 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Incentivized Learning in Principal-Agent Bandit Games.

Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus

CoRR, 2024

2023

Demonstration-Regularized RL.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

CoRR, 2023

Finite-Sample Analysis of the Temporal Difference Learning.

Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines

CoRR, 2023

Generative Flow Networks as Entropy-Regularized RL.

Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov

CoRR, 2023

Model-free Posterior Sampling via Learning Rate Randomization.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fast Rates for Maximum Entropy Exploration.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2023

Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold.

Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach, Éric Moulines

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Stochastic saddle-point optimization for the Wasserstein barycenter problem.

Daniil Tiapkin, Alexander V. Gasnikov, Pavel E. Dvurechensky

Optim. Lett., 2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses.

Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2022

Primal-Dual Stochastic Mirror Descent for MDPs.

Daniil Tiapkin, Alexander V. Gasnikov

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Improved Complexity Bounds in Wasserstein Barycenter Problem.

Darina Dvinskikh, Daniil Tiapkin

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Stochastic Saddle-Point Optimization for Wasserstein Barycenters.

Daniil Tiapkin, Alexander V. Gasnikov, Pavel E. Dvurechensky

CoRR, 2020