Spectral calibration of exponential Lévy models. - Denis Belomestny; Markus Reiß (Finance Stochastics, 2006)

Multiple stochastic volatility extension of the Libor market model and its implementation. - Denis Belomestny; Stanley Mathew; John Schoenmakers (Monte Carlo Methods Appl., 2009)

Regression Methods for Stochastic Control Problems and Their Convergence Analysis. - Denis Belomestny; Anastasia Kolodko; John Schoenmakers (SIAM J. Control. Optim., 2010)

Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates. - Denis Belomestny (Finance Stochastics, 2011)

Tight bounds for American options via multilevel Monte Carlo. - Denis Belomestny; Marcel Ladkau; John Schoenmakers (WSC, 2012)

Central Limit Theorems for Law-Invariant Coherent Risk Measures. - Denis Belomestny; Volker Krätschmer (J. Appl. Probab., 2012)

Multilevel dual approach for pricing American style derivatives. - Denis Belomestny; John Schoenmakers; Fabian Dickmann (Finance Stochastics, 2013)

Unbiased Simulation of Distributions with Explicitly Known Integral Transforms. - Denis Belomestny; Nan Chen; Yiwei Wang (MCQMC, 2014)

Addendum to: Multilevel dual approach for pricing American style derivatives. - Denis Belomestny; Mark S. Joshi; John Schoenmakers (Finance Stochastics, 2015)

Multilevel Simulation Based Policy Iteration for Optimal Stopping-Convergence and Complexity. - Denis Belomestny; Marcel Ladkau; John Schoenmakers (SIAM/ASA J. Uncertain. Quantification, 2015)

Pricing Bermudan Options via Multilevel Approximation Methods. - Denis Belomestny; Fabian Dickmann; Tigran Nagapetyan (SIAM J. Financial Math., 2015)

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 001 (ICASSP, 2017)

Optimal Stopping Under Probability Distortions. - Denis Belomestny; Volker Krätschmer (Math. Oper. Res., 2017)

Stratified regression-based variance reduction approach for weak approximation schemes. - Denis Belomestny; Stefan Häfner; Mikhail Urusov (Math. Comput. Simul., 2018)

Regression-Based Complexity Reduction of the Nested Monte Carlo Methods. - Denis Belomestny; Stefan Häfner; Mikhail Urusov (SIAM J. Financial Math., 2018)

Projected Particle Methods for Solving McKean-Vlasov Stochastic Differential Equations. - Denis Belomestny; John Schoenmakers (SIAM J. Numer. Anal., 2018)

Spectral Complexity Reduction of Music Signals for Cochlear Implant Users based on Subspace Tracking. - Johannes Gauer; Ekaterina A. Krymova; Denis Belomestny; Rainer Martin 001 (EUSIPCO, 2019)

Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm. - Denis Belomestny; Maxim Kaledin; John Schoenmakers (CoRR, 2019)

Iterative Multilevel density estimation for McKean-Vlasov SDEs via projections. - Denis Belomestny; Lukasz Szpruch; Shuren Tan (CoRR, 2019)

Minimax theorems for American options without time-consistency. - Denis Belomestny; Tobias Hübner; Volker Krätschmer; Sascha Nolte (Finance Stochastics, 2019)

Reinforced optimal control. - Christian Bayer 001; Denis Belomestny; Paul Hager; Paolo Pigato; John Schoenmakers; Vladimir G. Spokoiny (CoRR, 2020)

Variance reduction for Markov chains with application to MCMC. - Denis Belomestny; Leonid Iosipoi; Eric Moulines; Alexey Naumov; Sergey Samsonov (Stat. Comput., 2020)

Optimal Stopping of McKean-Vlasov Diffusions via Regression on Particle Systems. - Denis Belomestny; John Schoenmakers (SIAM J. Control. Optim., 2020)

Model-free policy evaluation in Reinforcement Learning via upper solutions. - Denis Belomestny; Ilya Levin; Eric Moulines; Alexey Naumov; Sergey Samsonov; Veronika Zorina (CoRR, 2021)

Ex2MCMC: Sampling through Exploration Exploitation. - Evgeny Lagutin; Daniil Selikhanovych; Achille Thin; Sergey Samsonov; Alexey Naumov; Denis Belomestny; Maxim Panov; Eric Moulines (CoRR, 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)

Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity. - Denis Belomestny; Leonid Iosipoi (Math. Comput. Simul., 2021)

Randomized Optimal Stopping Algorithms and Their Convergence Analysis. - Christian Bayer 001; Denis Belomestny; Paul Hager; Paolo Pigato; John Schoenmakers (SIAM J. Financial Math., 2021)

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 (ICML, 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 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization. - Maxim Kaledin; Alexander Golubev; Denis Belomestny (CoRR, 2022)

Primal-dual regression approach for Markov decision processes with general state and action space. - Denis Belomestny; John Schoenmakers (CoRR, 2022)

Variance reduction for additive functionals of Markov chains via martingale representations. - Denis Belomestny; Eric Moulines; Sergey Samsonov (Stat. Comput., 2022)

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 (ICML, 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 (NeurIPS, 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, 2023)

Solving Optimal Stopping Problems via Randomization and Empirical Dual Optimization. - Denis Belomestny; Christian Bender; John Schoenmakers (Math. Oper. Res., 2023)

Demonstration-Regularized RL. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (ICLR, 2024)

Weighted mesh algorithms for general Markov decision processes: Convergence and tractability. - Denis Belomestny; John Schoenmakers (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)

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)

Theoretical guarantees for neural control variates in MCMC. - Denis Belomestny; Artur Goldman; Alexey Naumov; Sergey Samsonov (Math. Comput. Simul., 2024)

Primal-Dual Regression Approach for Markov Decision Processes with General State and Action Spaces. - Denis Belomestny; John Schoenmakers (SIAM J. Control. Optim., 2024)

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. - Marina Sheshukova; Denis Belomestny; Alain Oliviero Durmus; Eric Moulines; Alexey Naumov; Sergey Samsonov (ICLR, 2025)

Weighted mesh algorithms for general Markov decision processes: Convergence and tractability. - Denis Belomestny; John Schoenmakers; Veronika Zorina (J. Complex., 2025)

Statistical Inference for Conservation Law McKean-Vlasov SDEs via Deep Neural Networks. - Denis Belomestny; Tatiana Orlova (SIAM/ASA J. Uncertain. Quantification, 2025)

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent. - Marina Sheshukova; Sergey Samsonov; Denis Belomestny; Eric Moulines; Qi-Man Shao; Zhuo-Song Zhang; Alexey Naumov (CoRR, 2025)

Accelerating Nash Learning from Human Feedback via Mirror Prox. - Daniil Tiapkin; Daniele Calandriello; Denis Belomestny; Eric Moulines; Alexey Naumov; Kashif Rasul; Michal Valko; Pierre Ménard (CoRR, 2025)

Sample complexity of Schrödinger potential estimation. - Nikita Puchkin; Iurii Pustovalov; Yuri Sapronov; Denis Suchkov; Alexey Naumov; Denis Belomestny (CoRR, 2025)

Forward Reverse Kernel Regression for the Schrödinger bridge problem. - Denis Belomestny; John Schoenmakers (CoRR, 2025)

Tight Bounds for Schrödinger Potential Estimation in Unpaired Image-to-Image Translation Problems. - Nikita Puchkin; Denis Suchkov; Alexey Naumov; Denis Belomestny (CoRR, 2025)