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ORCIDDaniil Tiapkin
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2020 – today
- 2025
[c17]Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold, Eric Moulines:
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. AISTATS 2025: 1315-1323- [c16]Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz:
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. AISTATS 2025: 3331-3339 - [c15]Timofei Gritsaev, Nikita Morozov, Sergey Samsonov, Daniil Tiapkin:
Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization. ICLR 2025 - [c14]Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov:
Revisiting Non-Acyclic GFlowNets in Discrete Environments. ICML 2025 - [c13]Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini, Mathieu Laurière, Eric Moulines:
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean Field Games. ICML 2025 - [c12]Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel:
On Teacher Hacking in Language Model Distillation. ICML 2025 - [i21]Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel:
On Teacher Hacking in Language Model Distillation. CoRR abs/2502.02671 (2025) - [i20]Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov
:
Revisiting Non-Acyclic GFlowNets in Discrete Environments. CoRR abs/2502.07735 (2025) - [i19]Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard:
Accelerating Nash Learning from Human Feedback via Mirror Prox. CoRR abs/2505.19731 (2025) - [i18]Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini, Mathieu Laurière, Eric Moulines:
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games. CoRR abs/2505.22781 (2025) - [i17]Safwan Labbi, Paul Mangold, Daniil Tiapkin, Eric Moulines:
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment. CoRR abs/2505.23459 (2025) - [i16]Timofei Gritsaev, Nikita Morozov, Kirill Tamogashev, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry P. Vetrov, Nikolay Malkin:
Adaptive Destruction Processes for Diffusion Samplers. CoRR abs/2506.01541 (2025) - [i15]Daniil Tiapkin, Artem Agarkov, Nikita Morozov, Ian Maksimov, Askar Tsyganov, Timofei Gritsaev, Sergey Samsonov:
gfnx: Fast and Scalable Library for Generative Flow Networks in JAX. CoRR abs/2511.16592 (2025) - 2024
- [c11]Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov:
Generative Flow Networks as Entropy-Regularized RL. AISTATS 2024: 4213-4221 - [c10]Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines:
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. COLT 2024: 4511-4547 - [c9]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. ICLR 2024 - [c8]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus:
Incentivized Learning in Principal-Agent Bandit Games. ICML 2024 - [i14]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus:
Incentivized Learning in Principal-Agent Bandit Games. CoRR abs/2403.03811 (2024) - [i13]Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry P. Vetrov:
Improving GFlowNets with Monte Carlo Tree Search. CoRR abs/2406.13655 (2024) - [i12]Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz:
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. CoRR abs/2407.05704 (2024) - [i11]Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko:
A New Bound on the Cumulant Generating Function of Dirichlet Processes. CoRR abs/2409.18621 (2024) - [i10]Timofei Gritsaev, Nikita Morozov, Sergey Samsonov, Daniil Tiapkin:
Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization. CoRR abs/2410.15474 (2024) - [i9]Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold, Eric Moulines:
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. CoRR abs/2410.22908 (2024) - 2023
- [c7]Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach, Éric Moulines:
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold. COLT 2023: 1228-1258 - [c6]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard:
Fast Rates for Maximum Entropy Exploration. ICML 2023: 34161-34221 - [c5]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS 2023 - [i8]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard:
Fast Rates for Maximum Entropy Exploration. CoRR abs/2303.08059 (2023) - [i7]Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov:
Generative Flow Networks as Entropy-Regularized RL. CoRR abs/2310.12934 (2023) - [i6]Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines:
Finite-Sample Analysis of the Temporal Difference Learning. CoRR abs/2310.14286 (2023) - [i5]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. CoRR abs/2310.17303 (2023) - [i4]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Model-free Posterior Sampling via Learning Rate Randomization. CoRR abs/2310.18186 (2023) - 2022
- [j1]Daniil Tiapkin, Alexander V. Gasnikov, Pavel E. Dvurechensky:
Stochastic saddle-point optimization for the Wasserstein barycenter problem. Optim. Lett. 16(7): 2145-2175 (2022) - [c4]Daniil Tiapkin, Alexander V. Gasnikov:
Primal-Dual Stochastic Mirror Descent for MDPs. AISTATS 2022: 9723-9740 - [c3]Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard:
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML 2022: 21380-21431 - [c2]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS 2022 - [i3]Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard:
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR abs/2205.07704 (2022) - [i2]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR abs/2209.14414 (2022) - 2021
- [c1]Darina Dvinskikh, Daniil Tiapkin:
Improved Complexity Bounds in Wasserstein Barycenter Problem. AISTATS 2021: 1738-1746 - 2020
- [i1]Daniil Tiapkin, Alexander V. Gasnikov, Pavel E. Dvurechensky:
Stochastic Saddle-Point Optimization for Wasserstein Barycenters. CoRR abs/2006.06763 (2020)
Coauthor Index
aka: Éric Moulines
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