Stochastic Saddle-Point Optimization for Wasserstein Barycenters. - Daniil Tiapkin; Alexander V. Gasnikov; Pavel E. Dvurechensky (CoRR, 2020)

Improved Complexity Bounds in Wasserstein Barycenter Problem. - Darina Dvinskikh; Daniil Tiapkin (AISTATS, 2021)

Primal-Dual Stochastic Mirror Descent for MDPs. - Daniil Tiapkin; Alexander V. Gasnikov (AISTATS, 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 (ICML, 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 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

Stochastic saddle-point optimization for the Wasserstein barycenter problem. - Daniil Tiapkin; Alexander V. Gasnikov; Pavel E. Dvurechensky (Optim. Lett., 2022)

Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold. - Sholom Schechtman; Daniil Tiapkin; Michael Muehlebach; Éric Moulines (COLT, 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 (ICML, 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 (NeurIPS, 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 (AISTATS, 2024)

Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. - Sergey Samsonov; Daniil Tiapkin; Alexey Naumov; Eric Moulines (COLT, 2024)

Demonstration-Regularized RL. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (ICLR, 2024)

Incentivized Learning in Principal-Agent Bandit Games. - Antoine Scheid; Daniil Tiapkin; Etienne Boursier; Aymeric Capitaine; Eric Moulines; Michael I. Jordan; El-Mahdi El-Mhamdi; Alain Oliviero Durmus (ICML, 2024)

Improving GFlowNets with Monte Carlo Tree Search. - Nikita Morozov; Daniil Tiapkin; Sergey Samsonov; Alexey Naumov; Dmitry P. Vetrov (CoRR, 2024)

A New Bound on the Cumulant Generating Function of Dirichlet Processes. - Pierre Perrault; Denis Belomestny; Pierre Ménard; Éric Moulines; Alexey Naumov; Daniil Tiapkin; Michal Valko (CoRR, 2024)

Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. - Safwan Labbi; Daniil Tiapkin; Lorenzo Mancini; Paul Mangold; Eric Moulines (AISTATS, 2025)

Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. - Daniil Tiapkin; Evgenii Chzhen; Gilles Stoltz (AISTATS, 2025)

Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization. - Timofei Gritsaev; Nikita Morozov; Sergey Samsonov; Daniil Tiapkin (ICLR, 2025)

On Teacher Hacking in Language Model Distillation. - Daniil Tiapkin; Daniele Calandriello; Johan Ferret; Sarah Perrin; Nino Vieillard; Alexandre Ramé; Mathieu Blondel (CoRR, 2025)

Revisiting Non-Acyclic GFlowNets in Discrete Environments. - Nikita Morozov; Ian Maksimov; Daniil Tiapkin; Sergey Samsonov (CoRR, 2025)

Accelerating Nash Learning from Human Feedback via Mirror Prox. - Daniil Tiapkin; Daniele Calandriello; Denis Belomestny; Eric Moulines; Alexey Naumov; Kashif Rasul; Michal Valko; Pierre Ménard (CoRR, 2025)

Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games. - Antonio Ocello; Daniil Tiapkin; Lorenzo Mancini; Mathieu Laurière; Eric Moulines (CoRR, 2025)

On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment. - Safwan Labbi; Paul Mangold; Daniil Tiapkin; Eric Moulines (CoRR, 2025)

Adaptive Destruction Processes for Diffusion Samplers. - Timofei Gritsaev; Nikita Morozov; Kirill Tamogashev; Daniil Tiapkin; Sergey Samsonov; Alexey Naumov; Dmitry P. Vetrov; Nikolay Malkin (CoRR, 2025)