Denis Belomestny

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 40

Top Topics

Markov Decision Process

Policy Iteration

Optimal Stopping

Top Venues

Math. Comput. Simul.

SIAM J. Financial Math.

Finance Stochastics

Publications

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. ICLR (2024)
Denis Belomestny, John Schoenmakers
Primal-Dual Regression Approach for Markov Decision Processes with General State and Action Spaces. SIAM J. Control. Optim. 62 (1) (2024)
Denis Belomestny, John Schoenmakers
Weighted mesh algorithms for general Markov decision processes: Convergence and tractability. CoRR (2024)
Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
Theoretical guarantees for neural control variates in MCMC. Math. Comput. Simul. 220 (2024)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. CoRR (2023)
Denis Belomestny, Christian Bender, John Schoenmakers
Solving Optimal Stopping Problems via Randomization and Empirical Dual Optimization. Math. Oper. Res. 48 (3) (2023)
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 (2023)
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 (2023)
Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
Theoretical guarantees for neural control variates in MCMC. CoRR (2023)
Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. Neural Networks 161 (2023)
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)
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)
Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. CoRR (2022)
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)
Denis Belomestny, Eric Moulines, Sergey Samsonov
Variance reduction for additive functionals of Markov chains via martingale representations. Stat. Comput. 32 (1) (2022)
Maxim Kaledin, Alexander Golubev, Denis Belomestny
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization. CoRR (2022)
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 (2022)
Denis Belomestny, John Schoenmakers
Primal-dual regression approach for Markov decision processes with general state and action space. CoRR (2022)
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 (2022)
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)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. SIAM/ASA J. Uncertain. Quantification 9 (2) (2021)
Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines
MCMC: Sampling through Exploration Exploitation. CoRR (2021)
Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina
Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR (2021)
Denis Belomestny, Leonid Iosipoi
Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity. Math. Comput. Simul. 181 (2021)
Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers
Randomized Optimal Stopping Algorithms and Their Convergence Analysis. SIAM J. Financial Math. 12 (3) (2021)
Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers
Randomized optimal stopping algorithms and their convergence analysis. CoRR (2020)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance reduction for Markov chains with application to MCMC. Stat. Comput. 30 (4) (2020)
Denis Belomestny, John Schoenmakers
Optimal Stopping of McKean-Vlasov Diffusions via Regression on Particle Systems. SIAM J. Control. Optim. 58 (1) (2020)
Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers, Vladimir G. Spokoiny
Reinforced optimal control. CoRR (2020)
Denis Belomestny, Tobias Hübner, Volker Krätschmer, Sascha Nolte
Minimax theorems for American options without time-consistency. Finance Stochastics 23 (1) (2019)
Denis Belomestny, Lukasz Szpruch, Shuren Tan
Iterative Multilevel density estimation for McKean-Vlasov SDEs via projections. CoRR (2019)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance reduction for Markov chains with application to MCMC. CoRR (2019)
Denis Belomestny, M. Kaledin, John Schoenmakers
Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm. CoRR (2019)
Johannes Gauer, Ekaterina A. Krymova, Denis Belomestny, Rainer Martin
Spectral Complexity Reduction of Music Signals for Cochlear Implant Users based on Subspace Tracking. EUSIPCO (2019)
Denis Belomestny, Leonid Iosipoi
Fourier transform MCMC, heavy tailed distributions and geometric ergodicity. CoRR (2019)
Denis Belomestny, Stefan Häfner, Mikhail Urusov
Stratified regression-based variance reduction approach for weak approximation schemes. Math. Comput. Simul. 143 (2018)
Denis Belomestny, John Schoenmakers
Projected Particle Methods for Solving McKean-Vlasov Stochastic Differential Equations. SIAM J. Numer. Anal. 56 (6) (2018)
Denis Belomestny, Stefan Häfner, Mikhail Urusov
Regression-Based Complexity Reduction of the Nested Monte Carlo Methods. SIAM J. Financial Math. 9 (2) (2018)
Denis Belomestny, Volker Krätschmer
Optimal Stopping Under Probability Distortions. Math. Oper. Res. 42 (3) (2017)
Ekaterina A. Krymova, Anil M. Nagathil, Denis Belomestny, Rainer Martin
Segmentation of music signals based on explained variance ratio for applications in spectral complexity reduction. ICASSP (2017)
Denis Belomestny, Marcel Ladkau, John Schoenmakers
Multilevel Simulation Based Policy Iteration for Optimal Stopping-Convergence and Complexity. SIAM/ASA J. Uncertain. Quantification 3 (1) (2015)
Denis Belomestny, Fabian Dickmann, Tigran Nagapetyan
Pricing Bermudan Options via Multilevel Approximation Methods. SIAM J. Financial Math. 6 (1) (2015)
Denis Belomestny, Mark S. Joshi, John Schoenmakers
Addendum to: Multilevel dual approach for pricing American style derivatives. Finance Stochastics 19 (3) (2015)
Denis Belomestny, Nan Chen, Yiwei Wang
Unbiased Simulation of Distributions with Explicitly Known Integral Transforms. MCQMC (2014)
Denis Belomestny, John Schoenmakers, Fabian Dickmann
Multilevel dual approach for pricing American style derivatives. Finance Stochastics 17 (4) (2013)
Denis Belomestny, Marcel Ladkau, John Schoenmakers
Tight bounds for American options via multilevel Monte Carlo. WSC (2012)
Denis Belomestny, Volker Krätschmer
Central Limit Theorems for Law-Invariant Coherent Risk Measures. J. Appl. Probab. 49 (1) (2012)
Denis Belomestny
Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates. Finance Stochastics 15 (4) (2011)
Denis Belomestny, Anastasia Kolodko, John Schoenmakers
Regression Methods for Stochastic Control Problems and Their Convergence Analysis. SIAM J. Control. Optim. 48 (5) (2010)
Denis Belomestny, Stanley Mathew, John Schoenmakers
Multiple stochastic volatility extension of the Libor market model and its implementation. Monte Carlo Methods Appl. 15 (4) (2009)
Denis Belomestny, Markus Reiß
Spectral calibration of exponential Lévy models. Finance Stochastics 10 (4) (2006)