Denis Belomestny

65 papers A* 6Misc 2Journal 55Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Schrödinger bridge problem via empirical risk minimization. CoRR Denis Belomestny, Alexey Naumov, Nikita Puchkin, Denis Suchkov
2026	J	`jnl`	UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms. J. Optim. Theory Appl. Denis Belomestny, Ilya Levin, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Accelerating Nash Learning from Human Feedback via Mirror Prox. CoRR Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard
2025	J	`jnl`	Forward Reverse Kernel Regression for the Schrödinger bridge problem. CoRR Denis Belomestny, John Schoenmakers
2025	J	`jnl`	Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent. CoRR Marina Sheshukova, Sergey Samsonov, Denis Belomestny, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov
2025	A*	`conf`	Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. ICLR Marina Sheshukova, Denis Belomestny, Alain Oliviero Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Sample complexity of Schrödinger potential estimation. CoRR Nikita Puchkin, Iurii Pustovalov, Yuri Sapronov, Denis Suchkov, Alexey Naumov, Denis Belomestny
2025	J	`jnl`	Statistical Inference for Conservation Law McKean-Vlasov SDEs via Deep Neural Networks. SIAM/ASA J. Uncertain. Quantification Denis Belomestny, Tatiana Orlova
2025	J	`jnl`	Statistical analysis of Inverse Entropy-regularized Reinforcement Learning. CoRR Denis Belomestny, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Tight Bounds for Schrödinger Potential Estimation in Unpaired Image-to-Image Translation Problems. CoRR Nikita Puchkin, Denis Suchkov, Alexey Naumov, Denis Belomestny
2025	J	`jnl`	Weighted mesh algorithms for general Markov decision processes: Convergence and tractability. J. Complex. Denis Belomestny, John Schoenmakers, Veronika Zorina
2024	J	`jnl`	A New Bound on the Cumulant Generating Function of Dirichlet Processes. CoRR Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko
2024	A*	`conf`	Demonstration-Regularized RL. ICLR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2024	J	`jnl`	Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. CoRR Marina Sheshukova, Denis Belomestny, Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2024	J	`jnl`	Primal-Dual Regression Approach for Markov Decision Processes with General State and Action Spaces. SIAM J. Control. Optim. Denis Belomestny, John Schoenmakers
2024	J	`jnl`	Rates of convergence for density estimation with generative adversarial networks. J. Mach. Learn. Res. Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov
2024	J	`jnl`	Theoretical guarantees for neural control variates in MCMC. Math. Comput. Simul. Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
2024	J	`jnl`	Weighted mesh algorithms for general Markov decision processes: Convergence and tractability. CoRR Denis Belomestny, John Schoenmakers
2023	J	`jnl`	Demonstration-Regularized RL. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	A*	`conf`	Fast Rates for Maximum Entropy Exploration. ICML Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Fast Rates for Maximum Entropy Exploration. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	A*	`conf`	Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Model-free Posterior Sampling via Learning Rate Randomization. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. Neural Networks Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov
2023	J	`jnl`	Solving Optimal Stopping Problems via Randomization and Empirical Dual Optimization. Math. Oper. Res. Denis Belomestny, Christian Bender, John Schoenmakers
2023	J	`jnl`	Theoretical guarantees for neural control variates in MCMC. CoRR Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
2022	A*	`conf`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	A*	`conf`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Primal-dual regression approach for Markov decision processes with general state and action space. CoRR Denis Belomestny, John Schoenmakers
2022	J	`jnl`	Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. CoRR Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov
2022	J	`jnl`	Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization. CoRR Maxim Kaledin, Alexander Golubev, Denis Belomestny
2022	J	`jnl`	Variance reduction for additive functionals of Markov chains via martingale representations. Stat. Comput. Denis Belomestny, Eric Moulines, Sergey Samsonov
2021	J	`jnl`	Ex CoRR Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines
2021	J	`jnl`	Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity. Math. Comput. Simul. Denis Belomestny, Leonid Iosipoi
2021	J	`jnl`	Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina
2021	J	`jnl`	Randomized Optimal Stopping Algorithms and Their Convergence Analysis. SIAM J. Financial Math. Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers
2021	J	`jnl`	Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. SIAM/ASA J. Uncertain. Quantification Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
2020	J	`jnl`	Optimal Stopping of McKean-Vlasov Diffusions via Regression on Particle Systems. SIAM J. Control. Optim. Denis Belomestny, John Schoenmakers
2020	J	`jnl`	Randomized optimal stopping algorithms and their convergence analysis. CoRR Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers
2020	J	`jnl`	Reinforced optimal control. CoRR Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers, Vladimir G. Spokoiny
2020	J	`jnl`	Variance reduction for Markov chains with application to MCMC. Stat. Comput. Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
2019	J	`jnl`	Fourier transform MCMC, heavy tailed distributions and geometric ergodicity. CoRR Denis Belomestny, Leonid Iosipoi
2019	J	`jnl`	Iterative Multilevel density estimation for McKean-Vlasov SDEs via projections. CoRR Denis Belomestny, Lukasz Szpruch, Shuren Tan
2019	J	`jnl`	Minimax theorems for American options without time-consistency. Finance Stochastics Denis Belomestny, Tobias Hübner, Volker Krätschmer, Sascha Nolte
2019	J	`jnl`	Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm. CoRR Denis Belomestny, Maxim Kaledin, John Schoenmakers
2019	—	`conf`	Spectral Complexity Reduction of Music Signals for Cochlear Implant Users based on Subspace Tracking. EUSIPCO Johannes Gauer, Ekaterina A. Krymova, Denis Belomestny, Rainer Martin
2019	J	`jnl`	Variance reduction for Markov chains with application to MCMC. CoRR Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
2018	J	`jnl`	Projected Particle Methods for Solving McKean-Vlasov Stochastic Differential Equations. SIAM J. Numer. Anal. Denis Belomestny, John Schoenmakers
2018	J	`jnl`	Regression-Based Complexity Reduction of the Nested Monte Carlo Methods. SIAM J. Financial Math. Denis Belomestny, Stefan Häfner, Mikhail Urusov
2018	J	`jnl`	Stratified regression-based variance reduction approach for weak approximation schemes. Math. Comput. Simul. Denis Belomestny, Stefan Häfner, Mikhail Urusov
2017	J	`jnl`	Optimal Stopping Under Probability Distortions. Math. Oper. Res. Denis Belomestny, Volker Krätschmer
2017	Misc	`conf`	Segmentation of music signals based on explained variance ratio for applications in spectral complexity reduction. ICASSP Ekaterina A. Krymova, Anil M. Nagathil, Denis Belomestny, Rainer Martin
2015	J	`jnl`	Addendum to: Multilevel dual approach for pricing American style derivatives. Finance Stochastics Denis Belomestny, Mark S. Joshi, John Schoenmakers
2015	J	`jnl`	Multilevel Simulation Based Policy Iteration for Optimal Stopping-Convergence and Complexity. SIAM/ASA J. Uncertain. Quantification Denis Belomestny, Marcel Ladkau, John Schoenmakers
2015	J	`jnl`	Pricing Bermudan Options via Multilevel Approximation Methods. SIAM J. Financial Math. Denis Belomestny, Fabian Dickmann, Tigran Nagapetyan
2014	—	`conf`	Unbiased Simulation of Distributions with Explicitly Known Integral Transforms. MCQMC Denis Belomestny, Nan Chen, Yiwei Wang
2013	J	`jnl`	Multilevel dual approach for pricing American style derivatives. Finance Stochastics Denis Belomestny, John Schoenmakers, Fabian Dickmann
2012	J	`jnl`	Central Limit Theorems for Law-Invariant Coherent Risk Measures. J. Appl. Probab. Denis Belomestny, Volker Krätschmer
2012	Misc	`conf`	Tight bounds for American options via multilevel Monte Carlo. WSC Denis Belomestny, Marcel Ladkau, John Schoenmakers
2011	J	`jnl`	Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates. Finance Stochastics Denis Belomestny
2010	J	`jnl`	Regression Methods for Stochastic Control Problems and Their Convergence Analysis. SIAM J. Control. Optim. Denis Belomestny, Anastasia Kolodko, John Schoenmakers
2009	J	`jnl`	Multiple stochastic volatility extension of the Libor market model and its implementation. Monte Carlo Methods Appl. Denis Belomestny, Stanley Mathew, John Schoenmakers
2006	J	`jnl`	Spectral calibration of exponential Lévy models. Finance Stochastics Denis Belomestny, Markus Reiß