Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. - Gilles Blanchard; Masashi Sugiyama; Motoaki Kawanabe; Vladimir G. Spokoiny; Klaus-Robert Müller (NIPS, 2005)

A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces. - Motoaki Kawanabe; Gilles Blanchard; Masashi Sugiyama; Vladimir G. Spokoiny; Klaus-Robert Müller (ICA, 2006)

Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis. - Masashi Sugiyama; Motoaki Kawanabe; Gilles Blanchard; Vladimir G. Spokoiny; Klaus-Robert Müller (ICASSP (3), 2006)

In Search of Non-Gaussian Components of a High-Dimensional Distribution. - Gilles Blanchard; Motoaki Kawanabe; Masashi Sugiyama; Vladimir G. Spokoiny; Klaus-Robert Müller (J. Mach. Learn. Res., 2006)

Analyzing fMRI experiments with structural adaptive smoothing procedures. - Karsten Tabelow; Jörg Polzehl; Henning U. Voss; Vladimir G. Spokoiny (NeuroImage, 2006)

Recovering convex edges of an image from noisy tomographic data. - Alexander Goldenshluger; Vladimir G. Spokoiny (IEEE Trans. Inf. Theory, 2006)

A New Algorithm for Estimating the Effective Dimension-Reduction Subspace. - Arnak S. Dalalyan; Anatoli B. Juditsky; Vladimir G. Spokoiny (J. Mach. Learn. Res., 2008)

Diffusion tensor imaging: Structural adaptive smoothing. - Karsten Tabelow; Jörg Polzehl; Vladimir G. Spokoiny; Henning U. Voss (NeuroImage, 2008)

Spatially Adaptive Estimation via Fitted Local Likelihood Techniques. - Vladimir Katkovnik; Vladimir G. Spokoiny (IEEE Trans. Signal Process., 2008)

Sparse non-Gaussian component analysis. - Elmar Diederichs; Anatoli B. Juditsky; Vladimir G. Spokoiny; Christof Schütte (IEEE Trans. Inf. Theory, 2010)

Properties of the posterior distribution of a regression model based on Gaussian random fields. - Alexey A. Zaitsev; Evgeny V. Burnaev; Vladimir G. Spokoiny (Autom. Remote. Control., 2013)

Uniform properties of the local maximum likelihood estimate. - Mayya M. Zhilova; Vladimir G. Spokoiny (Autom. Remote. Control., 2013)

Sparse non Gaussian component analysis by semidefinite programming. - Elmar Diederichs; Anatoli B. Juditsky; Arkadi Nemirovski; Vladimir G. Spokoiny (Mach. Learn., 2013)

Construction of Mean-Self-Financing Strategies for European Options under Regime-Switching. - G. N. Milstein; Vladimir G. Spokoiny (SIAM J. Financial Math., 2014)

Primal-Dual Methods for Solving Infinite-Dimensional Games. - Pavel E. Dvurechensky; Yurii E. Nesterov; Vladimir G. Spokoiny (J. Optim. Theory Appl., 2015)

Detection of homologous recombination in closely related strains. - Anastasia S. Kalinina; Alexandra L. Suvorikova; Vladimir G. Spokoiny; Mikhail S. Gelfand (J. Bioinform. Comput. Biol., 2016)

Convergence of an Alternating Maximization Procedure. - Andreas Andresen; Vladimir G. Spokoiny (J. Mach. Learn. Res., 2016)

Earthquake prediction using the fields estimated by an adaptive algorithm. - Valeri G. Gitis; Alexander B. Derendyaev; Sergey A. Pirogov; Vladimir G. Spokoiny; E. F. Yurkov (WIMS, 2017)

Random Gradient-Free Minimization of Convex Functions. - Yurii E. Nesterov; Vladimir G. Spokoiny (Found. Comput. Math., 2017)

Pointwise adaptation via stagewise aggregation of local estimates for multiclass classification. - Nikita Puchkin; Vladimir G. Spokoiny (CoRR, 2018)

Online Graph-Based Change-Point Detection for High Dimensional Data. - Yang-Wen Sun; Katerina Papagiannouli; Vladimir G. Spokoiny (CoRR, 2019)

Adaptive Nonparametric Clustering. - Kirill Efimov; Larisa Adamyan; Vladimir G. Spokoiny (IEEE Trans. Inf. Theory, 2019)

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

High dimensional change-point detection: a complete graph approach. - Yang-Wen Sun; Katerina Papagiannouli; Vladimir G. Spokoiny (CoRR, 2022)

Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation. - Vladimir G. Spokoiny (CoRR, 2022)

Structure-adaptive Manifold Estimation. - Nikita Puchkin; Vladimir G. Spokoiny (J. Mach. Learn. Res., 2022)

Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation. - Vladimir G. Spokoiny (SIAM/ASA J. Uncertain. Quantification, 2023)

Accelerated gradient methods with absolute and relative noise in the gradient. - Artem Vasin; Alexander V. Gasnikov; Pavel E. Dvurechensky; Vladimir G. Spokoiny (Optim. Methods Softw., 2023)

Dimension-free bounds in high-dimensional linear regression via error-in-operator approach. - Fedor Noskov; Nikita Puchkin; Vladimir G. Spokoiny (CoRR, 2025)