D. Belomestny

发表

Spatial aggregation of local likelihood estimates with applications to classification

V. Spokoiny, D. Belomestny, 2007, 0712.0939.

Rates of convergence for constrained deconvolution problem

D. Belomestny, 2003, math/0306237.

A. Naumov, S. Samsonov, D. Belomestny, 2020 .

Sobolev-Hermite versus Sobolev nonparametric density estimation on R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\

F. Comte, V. Genon-Catalot, D. Belomestny, 2017, Annals of the Institute of Statistical Mathematics.

MONTE CARLO EVALUATION OF AMERICAN OPTIONS USING CONSUMPTION PROCESSES

G. Milstein, D. Belomestny, 2006 .

TRUE UPPER BOUNDS FOR BERMUDAN PRODUCTS VIA NON‐NESTED MONTE CARLO

D. Belomestny, J. Schoenmakers, Christian Bender, 2009 .

Optimal Stopping via Pathwise Dual Empirical Maximisation

R. Hildebrand, D. Belomestny, J. Schoenmakers, 2014 .

Optimal stopping via deeply boosted backward regression

V. Spokoiny, D. Belomestny, J. Schoenmakers, 2018 .

Mathematisches Forschungsinstitut Oberwolfach New Inference Concepts for Analysing Complex Data

G. Blanchard, D. Belomestny, Organised, 2022 .

Optimal stopping via reinforced regression

Denis Belomestny, John Schoenmakers, Vladimir Spokoiny, 2018, Communications in Mathematical Sciences.

N A ] 7 A ug 2 01 8 Optimal stopping via deeply boosted backward regression

V. Spokoiny, D. Belomestny, J. Schoenmakers, 2018 .

Pricing American options via multi-level approximation methods

Denis Belomestny, Tigran Nagapetyan, Fabian Dickmann, 2013, 1303.1334.

Density deconvolution under general assumptions on the distribution of measurement errors

A. Goldenshluger, D. Belomestny, 2019, The Annals of Statistics.

Central Limit Theorems for Law-Invariant Coherent Risk Measures

Volker Krätschmer, D. Belomestny, 2012, Journal of Applied Probability.

Optimal stopping under probability distortions and law invariant coherent risk measures

D. Belomestny, Volker Kraetschmer, 2015, 1506.04439.

Optimal stopping under model uncertainty: randomized stopping times approach

Volker Krätschmer, D. Belomestny, 2014, 1405.2240.

Nonparametric Bayesian inference for L\'evy subordinators

P. Spreij, D. Belomestny, Moritz Schauer, 2018 .

Multilevel path simulation for weak approximation schemes with application to Lévy-driven SDEs

Denis Belomestny, Tigran Nagapetyan, Tigran Nagapetyan, 2017 .

Multilevel path simulation for weak approximation schemes

Denis Belomestny, Tigran Nagapetyan, Tigran Nagapetyan, 2014, 1406.2581.

Completion and continuation of nonlinear traffic time series: a probabilistic approach

Denis Belomestny, Michael Schreckenberg, M. Schreckenberg, 2003 .

Variance Reduction in Monte Carlo Estimators via Empirical Variance Minimization

Nikita Zhivotovskiy, D. Belomestny, L. Iosipoi, 2018, Doklady Mathematics.

Statistical inference for generalized Ornstein-Uhlenbeck processes

D. Belomestny, V. Panov, 2015, 1503.03381.

Sparse covariance matrix estimation in high-dimensional deconvolution

A. Tsybakov, D. Belomestny, Mathias Trabs, 2017, Bernoulli.

Variance reduction for MCMC methods via martingale representations

É. Moulines, D. Belomestny, M. Urusov, 2019 .

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

Rainer Martin, Denis Belomestny, Anil M. Nagathil, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Sparse constrained projection approximation subspace tracking.

D. Belomestny, E. Krymova, 2018, 1810.09298.

Spectral estimation of the fractional order of a Lévy process

D. Belomestny, 2010, 1001.1820.

A versatile deep-neural-network-based music preprocessing and remixing scheme for cochlear implant listeners.

D. Belomestny, A. Nagathil, J. Gauer, 2022, The Journal of the Acoustical Society of America.

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

Denis Belomestny, John Schoenmakers, Stanley Mathew, 2009, Monte Carlo Methods Appl..

PRICING CMS SPREAD OPTIONS IN A LIBOR MARKET MODEL

D. Belomestny, J. Schoenmakers, A. Kolodko, 2010 .

Pricing CMS spreads in the Libor market model

D. Belomestny, J. Schoenmakers, A. Kolodko, 2009 .

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

A. Naumov, S. Samsonov, D. Belomestny, 2022, ArXiv.

Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization

D. Belomestny, M. Kaledin, Alexander Golubev, 2022, ArXiv.

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses

É. Moulines, Pierre Ménard, A. Naumov, 2022, ICML.

Solving Optimal Stopping Problems via Randomization and Empirical Dual Optimization

D. Belomestny, J. Schoenmakers, Christian Bender, 2022, Math. Oper. Res..

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

A. Naumov, S. Samsonov, D. Belomestny, 2022, ArXiv.

Estimation of the activity of jumps for time-changed Lévy

D. Belomestny, V. Panov, 2012 .

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees

R. Munos, Mark Rowland, É. Moulines, 2022, NeurIPS.

Solving linear parabolic rough partial differential equations

S. Riedel, D. Belomestny, J. Schoenmakers, 2018, 1803.09488.

Ex2MCMC: Sampling through Exploration Exploitation pdf

Eric Moulines, Denis Belomestny, Alexey Naumov, 2021, ArXiv.

Ex2MCMC: Sampling through Exploration Exploitation

É. Moulines, Daniil Selikhanovych, A. Naumov, 2021, ArXiv.

Fourier transform MCMC, heavy tailed distributions and geometric ergodicity

D. Belomestny, L. Iosipoi, 2019, Math. Comput. Simul..

Optimal Stopping via Pathwise Dual Empirical Maximisation

R. Hildebrand, D. Belomestny, J. Schoenmakers, 2017, Applied Mathematics & Optimization.

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

A. Naumov, S. Samsonov, D. Belomestny, 2022, Neural Networks.

Variance reduction for Markov chains with application to MCMC

É. Moulines, S. Samsonov, D. Belomestny, 2020, Statistics and computing.

Variance reduction for additive functionals of Markov chains via martingale representations

É. Moulines, S. Samsonov, D. Belomestny, 2022, Statistics and Computing.

P RICING B ERMUDAN OPTIONS VIA MULTI-LEVEL APPROXIMATION METHODS 1

Tigran Nagapetyan, D. Belomestny, Fabian Dickmann, 2013 .

Regression methods in pricing American and Bermudan options using consumption processes

V. Spokoiny, G. Milstein, D. Belomestny, 2009 .

Adaptive Simulation Algorithms for Pricing American and Bermudan Options by Local Analysis of Financial Market

G. Milstein, D. Belomestny, 2006 .

Minimax theorems for American options without time-consistency

D. Belomestny, Volker Krätschmer, Tobias Hübner, 2018, Finance and Stochastics.

Solving optimal stopping problems under model uncertainty via empirical dual optimisation

Volker Krätschmer, D. Belomestny, Tobias Hübner, 2022, Finance and Stochastics.

Rates of convergence for density estimation with generative adversarial networks

É. Moulines, A. Naumov, S. Samsonov, 2021, 2102.00199.

Simulation Based Option Pricing

Denis Belomestny, Grigori N. Milstein, G. Milstein, 2009 .

Sensitivities for Bermudan options by regression methods

Denis Belomestny, John G. M. Schoenmakers, Grigori N. Milstein, 2007 .

Bayesian TVP-VARX models with time invariant long-run multipliers

D. Belomestny, A. Polbin, E. Krymova, 2021 .

Estimating TVP-VAR models with time invariant long-run multipliers

D. Belomestny, A. Polbin, E. Krymova, 2020, 2008.00718.

Optimal Stopping Under Probability Distortions

Denis Belomestny, Volker Krätschmer, Volker Krätschmer, 2017, Math. Oper. Res..

Reinforced optimal control

V. Spokoiny, D. Belomestny, J. Schoenmakers, 2020, Communications in Mathematical Sciences.

From optimal martingales to randomized dual optimal stopping

Denis Belomestny, John Schoenmakers, D. Belomestny, 2021, Quantitative Finance.

Semitractability of optimal stopping problems via a weighted stochastic mesh algorithm pdf

D. Belomestny, M. Kaledin, J. Schoenmakers, 2019, Mathematical Finance.

Pricing Bermudan Options via Multilevel Approximation Methods

Denis Belomestny, Tigran Nagapetyan, Fabian Dickmann, 2015, SIAM J. Financial Math..

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

Denis Belomestny, John Schoenmakers, Marcel Ladkau, 2015, SIAM/ASA J. Uncertain. Quantification.

Addendum to: Multilevel dual approach for pricing American style derivatives

Denis Belomestny, John Schoenmakers, Mark S. Joshi, 2015, Finance Stochastics.

Multilevel dual approach for pricing American style derivatives

D. Belomestny, J. Schoenmakers, Fabian Dickmann, 2013, Finance and Stochastics.

Multilevel dual approach for pricing American style derivatives

Denis Belomestny, John Schoenmakers, Fabian Dickmann, 2012, Finance Stochastics.

Tight bounds for American options via multilevel Monte Carlo

Denis Belomestny, John Schoenmakers, Marcel Ladkau, 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

Simulation based policy iteration for American style derivatives - A multilevel approach

Denis Belomestny, John G. M. Schoenmakers, Marcel Ladkau, 2012 .

Regression Methods for Stochastic Control Problems and Their Convergence Analysis

Denis Belomestny, John Schoenmakers, Anastasia Kolodko, 2009, SIAM J. Control. Optim..

Regression Methods for Stochastic Control Problems

Denis Belomestny, John Schoenmakers, Anastasia Kolodko, 2008 .

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

Denis Belomestny, D. Belomestny, 2009, 0907.5599.

Randomized optimal stopping algorithms and their convergence analysis pdf

Denis Belomestny, Christian Bayer, Paul Hager, 2020, SIAM J. Financial Math..

Pricing Bermudan options using regression: optimal rates of convergence for lower estimates

D. Belomestny, 2009 .

F. Comte, V. Genon-Catalot, D. Belomestny, 2017, Annals of the Institute of Statistical Mathematics.

Spectral estimation of the Lévy density in partially observed affine models

D. Belomestny, 2009, 0907.4865.

Rates of convergence for density estimation with GANs

É. Moulines, A. Naumov, S. Samsonov, 2021 .

Variance reduction via empirical variance minimization: convergence and complexity

D. Belomestny, L. Iosipoi, N. Zhivotovskiy, 2017 .

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

Denis Belomestny, John Schoenmakers, D. Belomestny, 2018, SIAM J. Control. Optim..

Projected Particle Methods for Solving McKean-Vlasov Stochastic Differential Equations

Denis Belomestny, John Schoenmakers, D. Belomestny, 2017, SIAM J. Numer. Anal..

Estimation and Calibration of Lévy Models via Fourier Methods

Denis Belomestny, Markus Reiß, D. Belomestny, 2015 .

Statistical inference for moving‐average Lévy‐driven processes: Fourier‐based approach

D. Belomestny, V. Panov, Tatiana Orlova, 2017, 1702.02794.

Low-frequency estimation of continuous-time moving average Lévy processes

D. Belomestny, V. Panov, Jeannette H. C. Woerner, 2016, Bernoulli.

A jump-diffusion Libor model and its robust calibration

Denis Belomestny, D. Belomestny, J. Schoenmakers, 2010 .

Statistical inference for time-changed Lévy processes via composite characteristic function estimation

D. Belomestny, 2010, 1003.0275.

Spectral calibration of exponential Lévy models

Denis Belomestny, Markus Reiß, D. Belomestny, 2006, Finance Stochastics.

Estimation of the activity of jumps in time-changed Levy models

D. Belomestny, V. Panov, 2013 .

Abelian theorems for stochastic volatility models with application to the estimation of jump activity

D. Belomestny, V. Panov, 2013 .

Nonparametric density estimation from observations with multiplicative measurement errors

A. Goldenshluger, D. Belomestny, 2017, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques.

Solving stochastic dynamic programs by convex optimization and simulation

Denis Belomestny, Christian Bender, Nikolaus Schweizer, 2014 .

SOLVING OPTIMAL STOPPING PROBLEMS VIA EMPIRICAL DUAL OPTIMIZATION

Denis Belomestny, D. Belomestny, 2013, 1309.2125.

On the rates of convergence of simulation based optimization algorithms for optimal stopping problems

Denis Belomestny, D. Belomestny, 2009, 0909.3570.

A stochastic volatility Libor model and its robust calibration

D. Belomestny, J. Schoenmakers, S. Matthew, 2007 .

Libor model and its robust alibration

D. Belomestny, Stanley Mathew, John S hoenmakers, 2008 .

Theoretical guarantees for neural control variates in MCMC

A. Naumov, S. Samsonov, D. Belomestny, 2023, ArXiv.

Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC

D. Belomestny, L. Iosipoi, E. Moulines, 2020, SIAM/ASA J. Uncertain. Quantification.

Variance reduction for Markov chains with application to MCMC

É. Moulines, A. Naumov, S. Samsonov, 2019, Statistics and Computing.

Empirical variance minimization with applications in variance reduction and optimal control

Nikita Zhivotovskiy, D. Belomestny, Q. Paris, 2017, Bernoulli.

Regression-Based Complexity Reduction of the Nested Monte Carlo Methods

Denis Belomestny, Stefan Häfner, Mikhail Urusov, 2016, SIAM Journal on Financial Mathematics.

Truncated control variates for weak approximation schemes

D. Belomestny, M. Urusov, Stefan Hafner, 2017, 1701.00273.

Variance reduction for discretised diffusions via regression

Denis Belomestny, Mikhail Urusov, Tigran Nagapetyan, 2015, 1510.03141.

Higher order variance reduction for discretised diffusions via regression

Tigran Nagapetyan, D. Belomestny, M. Urusov, 2015 .

Nonparametric Laguerre estimation in the multiplicative censoring model

F. Comte, V. Genon-Catalot, D. Belomestny, 2016 .

Laguerre estimation for k-monotone densities observed with noise

F. Comte, V. Genon-Catalot, D. Belomestny, 2016 .

Low-rank diffusion matrix estimation for high-dimensional time-changed Lévy processes

D. Belomestny, Mathias Trabs, M. Trabs, 2015, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques.

Unbiased Simulation of Distributions with Explicitly Known Integral Transforms

Nan Chen, Denis Belomestny, Yiwei Wang, 2014, MCQMC.

Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms

Pierre Ménard, A. Naumov, D. Belomestny, 2023, 2304.03056.

Generalized Post-Widder inversion formula with application to statistics

D. Belomestny, J. Schoenmakers, H. Mai, 2015, 1511.09298.

Fast Rates for Maximum Entropy Exploration

R. Munos, É. Moulines, Daniele Calandriello, 2023, ICML.

Estimating the distribution of jumps in regular affine models: uniform rates of convergence

D. Belomestny, 2009 .

Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization

D. Belomestny, Alexander Golubev, Maxim Kaledin, 2022, ArXiv.

UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms

D. Belomestny, E. Moulines, S. Samsonov, 2021, 2105.02135.

SIEVE ESTIMATION OF THE MINIMAL ENTROPY MARTINGALE MARGINAL DENSITY WITH APPLICATION TO PRICING KERNEL ESTIMATION

W. Härdle, D. Belomestny, E. Krymova, 2017 .

Statistical inference for multidimensional time-changed L\'evy processes based on low-frequency data

D. Belomestny, 2010 .

Stability of Characterization of the Independence of Random Variables by the Independence of Linear Statistics

A. V. Prokhorov, D. Belomestny, 2015 .

Optimal Stopping of Integral Functionals and a “No-Loss” Free Boundary Formulation

L. Rüschendorf, D. Belomestny, M. Urusov, 2010 .

Optimal stopping of integral functionals and a “no-loss” free boundary formulation@@@Optimal Stopping of Integral Functionals and a “No-Loss” Free Boundary Formulation

L. Rüschendorf, D. Belomestny, Денис Витальевич Беломестный, 2009 .

Nonparametric Bayesian inference for stochastic processes with piecewise constant priors

P. Spreij, D. Belomestny, F. Meulen, 2023, 2305.07432.

Weak solutions to gamma-driven stochastic differential equations

P. Spreij, D. Belomestny, Moritz Schauer, 2021, Indagationes Mathematicae.

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

Denis Belomestny, Shuren Tan, Lukasz Szpruch, 2019, ArXiv.

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

Rainer Martin, Denis Belomestny, Johannes Gauer, 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

Semiparametric estimation of McKean–Vlasov SDEs

Denis Belomestny, Mark Podolskij, Vytaut.e Pilipauskait.e, 2021, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques.

Projected particle methods for solving McKean--Vlaslov equations

D. Belomestny, J. Schoenmakers, 2016 .

An iterative procedure for solving integral equations related to optimal stopping problems

Denis Belomestny, Pavel V. Gapeev, D. Belomestny, 2010 .

Statistical inference for exponential functionals of L\'evy processes

D. Belomestny, V. Panov, 2013, 1312.4731.

Minimax theorems for American options without time-consistency

Denis Belomestny, Volker Krätschmer, Tobias Hübner, 2018, Finance Stochastics.

Minimax theorems for American options in incomplete markets without time-consistency

D. Belomestny, Volker Kraetschmer, 2017, 1708.08904.