Denis Belomestny

According to our database, Denis Belomestny authored at least 44 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Primal-Dual Regression Approach for Markov Decision Processes with General State and Action Spaces.

Denis Belomestny, John Schoenmakers

SIAM J. Control. Optim., February, 2024

Theoretical guarantees for neural control variates in MCMC.

Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov

Math. Comput. Simul., 2024

Rates of convergence for density estimation with generative adversarial networks.

Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov

J. Mach. Learn. Res., 2024

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation.

Marina Sheshukova, Denis Belomestny, Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov

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

Weighted mesh algorithms for general Markov decision processes: Convergence and tractability.

Denis Belomestny, John Schoenmakers

CoRR, 2024

Demonstration-Regularized RL.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Solving Optimal Stopping Problems via Randomization and Empirical Dual Optimization.

Denis Belomestny, Christian Bender, John Schoenmakers

Math. Oper. Res., August, 2023

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations.

Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov

Neural Networks, April, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the International Conference on Machine Learning, 2023

2022

Variance reduction for additive functionals of Markov chains via martingale representations.

Denis Belomestny, Eric Moulines, Sergey Samsonov

Stat. Comput., 2022

Primal-dual regression approach for Markov decision processes with general state and action space.

Denis Belomestny, John Schoenmakers

CoRR, 2022

Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization.

Maxim Kaledin, Alexander Golubev, Denis Belomestny

CoRR, 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, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the International Conference on Machine Learning, 2022

2021

Randomized Optimal Stopping Algorithms and Their Convergence Analysis.

Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers

SIAM J. Financial Math., 2021

Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity.

Denis Belomestny, Leonid Iosipoi

Math. Comput. Simul., 2021

Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC.

Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov

SIAM/ASA J. Uncertain. Quantification, 2021

Ex²MCMC: Sampling through Exploration Exploitation.

Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines

CoRR, 2021

Model-free policy evaluation in Reinforcement Learning via upper solutions.

Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina

CoRR, 2021

2020

Optimal Stopping of McKean-Vlasov Diffusions via Regression on Particle Systems.

Denis Belomestny, John Schoenmakers

SIAM J. Control. Optim., 2020

Variance reduction for Markov chains with application to MCMC.

Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov

Stat. Comput., 2020

Reinforced optimal control.

Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers, Vladimir G. Spokoiny

CoRR, 2020

2019

Minimax theorems for American options without time-consistency.

Denis Belomestny, Tobias Hübner, Volker Krätschmer, Sascha Nolte

Finance Stochastics, 2019

Iterative Multilevel density estimation for McKean-Vlasov SDEs via projections.

Denis Belomestny, Lukasz Szpruch, Shuren Tan

CoRR, 2019

Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm.

Denis Belomestny, Maxim Kaledin, John Schoenmakers

CoRR, 2019

Spectral Complexity Reduction of Music Signals for Cochlear Implant Users based on Subspace Tracking.

Johannes Gauer, Ekaterina A. Krymova, Denis Belomestny, Rainer Martin

Proceedings of the 27th European Signal Processing Conference, 2019

2018

Projected Particle Methods for Solving McKean-Vlasov Stochastic Differential Equations.

Denis Belomestny, John Schoenmakers

SIAM J. Numer. Anal., 2018

Regression-Based Complexity Reduction of the Nested Monte Carlo Methods.

Denis Belomestny, Stefan Häfner, Mikhail Urusov

SIAM J. Financial Math., 2018

Stratified regression-based variance reduction approach for weak approximation schemes.

Denis Belomestny, Stefan Häfner, Mikhail Urusov

Math. Comput. Simul., 2018

2017

Optimal Stopping Under Probability Distortions.

Denis Belomestny, Volker Krätschmer

Math. Oper. Res., 2017

Segmentation of music signals based on explained variance ratio for applications in spectral complexity reduction.

Ekaterina A. Krymova, Anil M. Nagathil, Denis Belomestny, Rainer Martin

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2015

Pricing Bermudan Options via Multilevel Approximation Methods.

Denis Belomestny, Fabian Dickmann, Tigran Nagapetyan

SIAM J. Financial Math., 2015

Multilevel Simulation Based Policy Iteration for Optimal Stopping-Convergence and Complexity.

Denis Belomestny, Marcel Ladkau, John Schoenmakers

SIAM/ASA J. Uncertain. Quantification, 2015

Addendum to: Multilevel dual approach for pricing American style derivatives.

Denis Belomestny, Mark S. Joshi, John Schoenmakers

Finance Stochastics, 2015

2014

Unbiased Simulation of Distributions with Explicitly Known Integral Transforms.

Denis Belomestny, Nan Chen, Yiwei Wang

Proceedings of the Monte Carlo and Quasi-Monte Carlo Methods, 2014

2013

Multilevel dual approach for pricing American style derivatives.

Denis Belomestny, John Schoenmakers, Fabian Dickmann

Finance Stochastics, 2013

2012

Central Limit Theorems for Law-Invariant Coherent Risk Measures.

Denis Belomestny, Volker Krätschmer

J. Appl. Probab., 2012

Tight bounds for American options via multilevel Monte Carlo.

Denis Belomestny, Marcel Ladkau, John Schoenmakers

Proceedings of the Winter Simulation Conference, 2012

2011

Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates.

Denis Belomestny

Finance Stochastics, 2011

2010

Regression Methods for Stochastic Control Problems and Their Convergence Analysis.

Denis Belomestny, Anastasia Kolodko, John Schoenmakers

SIAM J. Control. Optim., 2010

2009

Multiple stochastic volatility extension of the Libor market model and its implementation.

Denis Belomestny, Stanley Mathew, John Schoenmakers

Monte Carlo Methods Appl., 2009

2006

Spectral calibration of exponential Lévy models.

Denis Belomestny, Markus Reiß

Finance Stochastics, 2006