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ORCIDVladimir G. Spokoiny
Person information
- affiliation: Moscow Institute of Physics and Technology, Laboratory of Structural Methods of Data Analysis in Predictive Modeling, Moscow, Russia
- affiliation: Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
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2020 – today
- 2023
[j19]Vladimir G. Spokoiny
:
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation. SIAM/ASA J. Uncertain. Quantification 11(3): 1044-1068 (2023)- [j18]Artem Vasin, Alexander V. Gasnikov, Pavel E. Dvurechensky, Vladimir G. Spokoiny:
Accelerated gradient methods with absolute and relative noise in the gradient. Optim. Methods Softw. 38(6): 1180-1229 (2023) - 2022
- [j17]Nikita Puchkin, Vladimir G. Spokoiny:
Structure-adaptive Manifold Estimation. J. Mach. Learn. Res. 23: 40:1-40:62 (2022) - [i5]Yang-Wen Sun, Katerina Papagiannouli, Vladimir G. Spokoiny:
High dimensional change-point detection: a complete graph approach. CoRR abs/2203.08709 (2022) - [i4]Vladimir G. Spokoiny:
Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation. CoRR abs/2204.11038 (2022) - 2020
- [i3]Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers, Vladimir G. Spokoiny:
Reinforced optimal control. CoRR abs/2011.12382 (2020)
2010 – 2019
- 2019
- [j16]Kirill Efimov, Larisa Adamyan, Vladimir G. Spokoiny:
Adaptive Nonparametric Clustering. IEEE Trans. Inf. Theory 65(8): 4875-4892 (2019) - [i2]Yang-Wen Sun, Katerina Papagiannouli, Vladimir G. Spokoiny:
Online Graph-Based Change-Point Detection for High Dimensional Data. CoRR abs/1906.03001 (2019) - 2018
- [i1]Nikita Puchkin, Vladimir G. Spokoiny:
Pointwise adaptation via stagewise aggregation of local estimates for multiclass classification. CoRR abs/1804.02756 (2018) - 2017
- [j15]Yurii E. Nesterov, Vladimir G. Spokoiny:
Random Gradient-Free Minimization of Convex Functions. Found. Comput. Math. 17(2): 527-566 (2017) - [c4]Valeri G. Gitis, Alexander B. Derendyaev, Sergey A. Pirogov, Vladimir G. Spokoiny, E. F. Yurkov:
Earthquake prediction using the fields estimated by an adaptive algorithm. WIMS 2017: 30:1-30:8 - 2016
- [j14]Anastasia S. Kalinina, Alexandra L. Suvorikova, Vladimir G. Spokoiny, Mikhail S. Gelfand:
Detection of homologous recombination in closely related strains. J. Bioinform. Comput. Biol. 14(2): 1641001:1-1641001:12 (2016) - [j13]Andreas Andresen, Vladimir G. Spokoiny:
Convergence of an Alternating Maximization Procedure. J. Mach. Learn. Res. 17: 63:1-63:53 (2016) - 2015
- [j12]Pavel E. Dvurechensky, Yurii E. Nesterov, Vladimir G. Spokoiny:
Primal-Dual Methods for Solving Infinite-Dimensional Games. J. Optim. Theory Appl. 166(1): 23-51 (2015) - 2014
- [j11]G. N. Milstein, Vladimir G. Spokoiny:
Construction of Mean-Self-Financing Strategies for European Options under Regime-Switching. SIAM J. Financial Math. 5(1): 532-556 (2014) - 2013
- [j10]Alexey A. Zaitsev, Evgeny V. Burnaev, Vladimir G. Spokoiny:
Properties of the posterior distribution of a regression model based on Gaussian random fields. Autom. Remote. Control. 74(10): 1645-1655 (2013) - [j9]Mayya M. Zhilova, Vladimir G. Spokoiny:
Uniform properties of the local maximum likelihood estimate. Autom. Remote. Control. 74(10): 1656-1669 (2013) - [j8]Elmar Diederichs, Anatoli B. Juditsky, Arkadi Nemirovski, Vladimir G. Spokoiny:
Sparse non Gaussian component analysis by semidefinite programming. Mach. Learn. 91(2): 211-238 (2013) - 2010
- [j7]Elmar Diederichs, Anatoli B. Juditsky, Vladimir G. Spokoiny, Christof Schütte:
Sparse non-Gaussian component analysis. IEEE Trans. Inf. Theory 56(6): 3033-3047 (2010)
2000 – 2009
- 2008
- [j6]Arnak S. Dalalyan, Anatoli B. Juditsky, Vladimir G. Spokoiny:
A New Algorithm for Estimating the Effective Dimension-Reduction Subspace. J. Mach. Learn. Res. 9: 1647-1678 (2008) - [j5]Karsten Tabelow, Jörg Polzehl, Vladimir G. Spokoiny, Henning U. Voss:
Diffusion tensor imaging: Structural adaptive smoothing. NeuroImage 39(4): 1763-1773 (2008) - [j4]Vladimir Katkovnik, Vladimir G. Spokoiny:
Spatially Adaptive Estimation via Fitted Local Likelihood Techniques. IEEE Trans. Signal Process. 56(3): 873-886 (2008) - 2006
- [j3]Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller:
In Search of Non-Gaussian Components of a High-Dimensional Distribution. J. Mach. Learn. Res. 7: 247-282 (2006) - [j2]Karsten Tabelow, Jörg Polzehl, Henning U. Voss, Vladimir G. Spokoiny:
Analyzing fMRI experiments with structural adaptive smoothing procedures. NeuroImage 33(1): 55-62 (2006) - [j1]Alexander Goldenshluger, Vladimir G. Spokoiny:
Recovering convex edges of an image from noisy tomographic data. IEEE Trans. Inf. Theory 52(4): 1322-1334 (2006) - [c3]Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller:
A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces. ICA 2006: 149-156 - [c2]Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Vladimir G. Spokoiny, Klaus-Robert Müller:
Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis. ICASSP (3) 2006: 608-611 - 2005
- [c1]Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir G. Spokoiny, Klaus-Robert Müller:
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. NIPS 2005: 131-138
Coauthor Index
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