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ORCIDDenis Belomestny
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2020 – today
- 2024
[j25]Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov:
Rates of convergence for density estimation with generative adversarial networks. J. Mach. Learn. Res. 25: 29:1-29:47 (2024)- [j24]Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
:
Theoretical guarantees for neural control variates in MCMC. Math. Comput. Simul. 220: 382-405 (2024) - [j23]Denis Belomestny, John Schoenmakers:
Primal-Dual Regression Approach for Markov Decision Processes with General State and Action Spaces. SIAM J. Control. Optim. 62(1): 650-679 (2024) - [c9]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. ICLR 2024 - [i19]Denis Belomestny, John Schoenmakers:
Weighted mesh algorithms for general Markov decision processes: Convergence and tractability. CoRR abs/2407.00388 (2024) - [i18]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) - 2023
- [j22]Denis Belomestny, Christian Bender, John Schoenmakers:
Solving Optimal Stopping Problems via Randomization and Empirical Dual Optimization. Math. Oper. Res. 48(3): 1454-1480 (2023) - [j21]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: 242-253 (2023) - [c8]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 - [c7]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 - [i17]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) - [i16]Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov:
Theoretical guarantees for neural control variates in MCMC. CoRR abs/2304.01111 (2023) - [i15]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. CoRR abs/2310.17303 (2023) - [i14]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
- [j20]Denis Belomestny, Eric Moulines, Sergey Samsonov:
Variance reduction for additive functionals of Markov chains via martingale representations. Stat. Comput. 32(1): 16 (2022) - [c6]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 - [c5]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 - [i13]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) - [i12]Maxim Kaledin, Alexander Golubev, Denis Belomestny:
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization. CoRR abs/2206.06827 (2022) - [i11]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 abs/2206.09527 (2022) - [i10]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) - [i9]Denis Belomestny, John Schoenmakers:
Primal-dual regression approach for Markov decision processes with general state and action space. CoRR abs/2210.00258 (2022) - 2021
- [j19]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): 507-535 (2021) - [j18]Denis Belomestny, Leonid Iosipoi:
Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity. Math. Comput. Simul. 181: 351-363 (2021) - [j17]Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers:
Randomized Optimal Stopping Algorithms and Their Convergence Analysis. SIAM J. Financial Math. 12(3): 1201-1225 (2021) - [i8]Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina:
Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR abs/2105.02135 (2021) - [i7]Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines:
Ex2MCMC: Sampling through Exploration Exploitation. CoRR abs/2111.02702 (2021) - 2020
- [j16]Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Variance reduction for Markov chains with application to MCMC. Stat. Comput. 30(4): 973-997 (2020) - [j15]Denis Belomestny, John Schoenmakers:
Optimal Stopping of McKean-Vlasov Diffusions via Regression on Particle Systems. SIAM J. Control. Optim. 58(1): 529-550 (2020) - [i6]Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers:
Randomized optimal stopping algorithms and their convergence analysis. CoRR abs/2002.00816 (2020) - [i5]Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers, Vladimir G. Spokoiny:
Reinforced optimal control. CoRR abs/2011.12382 (2020)
2010 – 2019
- 2019
- [j14]Denis Belomestny, Tobias Hübner, Volker Krätschmer, Sascha Nolte:
Minimax theorems for American options without time-consistency. Finance Stochastics 23(1): 209-238 (2019) - [c4]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: 1-5 - [i4]Denis Belomestny, Maxim Kaledin, John Schoenmakers:
Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm. CoRR abs/1906.09431 (2019) - [i3]Denis Belomestny, Leonid Iosipoi:
Fourier transform MCMC, heavy tailed distributions and geometric ergodicity. CoRR abs/1909.00698 (2019) - [i2]Denis Belomestny, Lukasz Szpruch, Shuren Tan:
Iterative Multilevel density estimation for McKean-Vlasov SDEs via projections. CoRR abs/1909.11717 (2019) - [i1]Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Variance reduction for Markov chains with application to MCMC. CoRR abs/1910.03643 (2019) - 2018
- [j13]Denis Belomestny, Stefan Häfner, Mikhail Urusov:
Stratified regression-based variance reduction approach for weak approximation schemes. Math. Comput. Simul. 143: 125-137 (2018) - [j12]Denis Belomestny, Stefan Häfner, Mikhail Urusov:
Regression-Based Complexity Reduction of the Nested Monte Carlo Methods. SIAM J. Financial Math. 9(2): 665-689 (2018) - [j11]Denis Belomestny, John Schoenmakers:
Projected Particle Methods for Solving McKean-Vlasov Stochastic Differential Equations. SIAM J. Numer. Anal. 56(6): 3169-3195 (2018) - 2017
- [j10]Denis Belomestny, Volker Krätschmer:
Optimal Stopping Under Probability Distortions. Math. Oper. Res. 42(3): 806-833 (2017) - [c3]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: 206-210 - 2015
- [j9]Denis Belomestny, Mark S. Joshi, John Schoenmakers:
Addendum to: Multilevel dual approach for pricing American style derivatives. Finance Stochastics 19(3): 681-684 (2015) - [j8]Denis Belomestny, Marcel Ladkau, John Schoenmakers:
Multilevel Simulation Based Policy Iteration for Optimal Stopping-Convergence and Complexity. SIAM/ASA J. Uncertain. Quantification 3(1): 460-483 (2015) - [j7]Denis Belomestny, Fabian Dickmann, Tigran Nagapetyan:
Pricing Bermudan Options via Multilevel Approximation Methods. SIAM J. Financial Math. 6(1): 448-466 (2015) - 2014
- [c2]Denis Belomestny, Nan Chen, Yiwei Wang:
Unbiased Simulation of Distributions with Explicitly Known Integral Transforms. MCQMC 2014: 229-244 - 2013
- [j6]Denis Belomestny, John Schoenmakers, Fabian Dickmann:
Multilevel dual approach for pricing American style derivatives. Finance Stochastics 17(4): 717-742 (2013) - 2012
- [j5]Denis Belomestny, Volker Krätschmer:
Central Limit Theorems for Law-Invariant Coherent Risk Measures. J. Appl. Probab. 49(1): 1-21 (2012) - [c1]Denis Belomestny, Marcel Ladkau, John Schoenmakers:
Tight bounds for American options via multilevel Monte Carlo. WSC 2012: 31:1-31:8 - 2011
- [j4]Denis Belomestny:
Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates. Finance Stochastics 15(4): 655-683 (2011) - 2010
- [j3]Denis Belomestny, Anastasia Kolodko, John Schoenmakers:
Regression Methods for Stochastic Control Problems and Their Convergence Analysis. SIAM J. Control. Optim. 48(5): 3562-3588 (2010)
2000 – 2009
- 2009
- [j2]Denis Belomestny, Stanley Mathew, John Schoenmakers:
Multiple stochastic volatility extension of the Libor market model and its implementation. Monte Carlo Methods Appl. 15(4): 285-310 (2009) - 2006
- [j1]Denis Belomestny, Markus Reiß:
Spectral calibration of exponential Lévy models. Finance Stochastics 10(4): 449-474 (2006)
Coauthor Index
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