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Arnak Dalalyan

Personal Details

First Name:Arnak
Middle Name:
Last Name:Dalalyan
Suffix:
RePEc Short-ID:pda587
[This author has chosen not to make the email address public]
http://arnak-dalalyan.fr

Affiliation

Centre de Recherche en Économie et Statistique (CREST)

Palaiseau, France
http://crest.science/
RePEc:edi:crestfr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Arnak Dalalyan, 2017. "Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent," Working Papers 2017-21, Center for Research in Economics and Statistics.
  2. Arnak Dalalyan & Mehdi Sebbar, 2017. "Optimal Kullback-Leibler Aggregation in Mixture Estimation by Maximum Likelihood," Working Papers 2017-22, Center for Research in Economics and Statistics.
  3. Olivier Collier & Arnak Dalalyan, 2017. "Estimating linear functionals of a sparse family of Poisson means Price Discrimination," Working Papers 2017-19, Center for Research in Economics and Statistics.
  4. Arnak Dalalyan & Avetik Karagulyan, 2017. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Working Papers 2017-20, Center for Research in Economics and Statistics.
  5. Arnak S. Dalalyan, 2014. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Working Papers 2014-45, Center for Research in Economics and Statistics.
  6. Arnak S. Dalalyan & Mohamed Hebiri & Johannes Lederer, 2014. "On the Prediction Performance of the Lasso," Working Papers 2014-05, Center for Research in Economics and Statistics.
  7. Olivier Collier & Arnak S, Dalalyan, 2013. "Minimax Rates in Permutation Estimation for Feature Matching," Working Papers 2013-34, Center for Research in Economics and Statistics.
  8. Olivier Collier & Arnak S, Dalalyan, 2013. "Curve registration by Nonparametric goodness-of-fit Testing," Working Papers 2013-33, Center for Research in Economics and Statistics.
  9. Laetitia Comminges & Arnak Dalalyan, 2012. "Minimax Testing of a Composite null Hypothesis Defined via a Quadratic Functional in the Model of regression," Working Papers 2012-19, Center for Research in Economics and Statistics.
  10. Arnak Dalalyan & Yuri Ingster & Alexandre B. Tsybakov, 2012. "Statistical Inference in Compound Functional Models," Working Papers 2012-20, Center for Research in Economics and Statistics.

Articles

  1. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
  2. Olivier Collier & Arnak S. Dalalyan, 2018. "Estimating linear functionals of a sparse family of Poisson means," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 331-344, July.
  3. Arnak S. Dalalyan, 2017. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 651-676, June.
  4. Dalalyan Arnak S. & Kutoyants Yury A., 2004. "On second order minimax estimation of invariant density for ergodic diffusion," Statistics & Risk Modeling, De Gruyter, vol. 22(1), pages 17-42, January.
  5. Arnak Dalalyan & Yury Kutoyants, 2003. "Asymptotically Efficient Estimation of the Derivative of the Invariant Density," Statistical Inference for Stochastic Processes, Springer, vol. 6(1), pages 89-107, January.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Arnak Dalalyan, 2017. "Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent," Working Papers 2017-21, Center for Research in Economics and Statistics.

    Cited by:

    1. Vincent Lemaire & Gilles Pag`es & Christian Yeo, 2023. "Swing contract pricing: with and without Neural Networks," Papers 2306.03822, arXiv.org, revised Mar 2024.
    2. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
    3. Ghaderi, Susan & Ahookhosh, Masoud & Arany, Adam & Skupin, Alexander & Patrinos, Panagiotis & Moreau, Yves, 2024. "Smoothing unadjusted Langevin algorithms for nonsmooth composite potential functions," Applied Mathematics and Computation, Elsevier, vol. 464(C).
    4. Florian Maire & Nial Friel & Pierre ALQUIER, 2017. "Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets," Working Papers 2017-40, Center for Research in Economics and Statistics.
    5. Chau, Huy N. & Rásonyi, Miklós, 2022. "Stochastic Gradient Hamiltonian Monte Carlo for non-convex learning," Stochastic Processes and their Applications, Elsevier, vol. 149(C), pages 341-368.

  2. Arnak Dalalyan & Avetik Karagulyan, 2017. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Working Papers 2017-20, Center for Research in Economics and Statistics.

    Cited by:

    1. Crespo, Marelys & Gadat, Sébastien & Gendre, Xavier, 2023. "Stochastic Langevin Monte Carlo for (weakly) log-concave posterior distributions," TSE Working Papers 23-1398, Toulouse School of Economics (TSE).
    2. Peiyi Zhang & Tianning Dong & Faming Liang, 2024. "An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems," Computational Statistics, Springer, vol. 39(6), pages 3347-3372, September.
    3. Yang, Jun & Roberts, Gareth O. & Rosenthal, Jeffrey S., 2020. "Optimal scaling of random-walk metropolis algorithms on general target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(10), pages 6094-6132.
    4. Ghaderi, Susan & Ahookhosh, Masoud & Arany, Adam & Skupin, Alexander & Patrinos, Panagiotis & Moreau, Yves, 2024. "Smoothing unadjusted Langevin algorithms for nonsmooth composite potential functions," Applied Mathematics and Computation, Elsevier, vol. 464(C).
    5. Murray Pollock & Paul Fearnhead & Adam M. Johansen & Gareth O. Roberts, 2020. "Quasi‐stationary Monte Carlo and the ScaLE algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1167-1221, December.
    6. Sotirios Sabanis & Ying Zhang, 2020. "A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating," Papers 2007.01672, arXiv.org.
    7. Chau, Huy N. & Rásonyi, Miklós, 2022. "Stochastic Gradient Hamiltonian Monte Carlo for non-convex learning," Stochastic Processes and their Applications, Elsevier, vol. 149(C), pages 341-368.

  3. Arnak S. Dalalyan, 2014. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Working Papers 2014-45, Center for Research in Economics and Statistics.

    Cited by:

    1. Ruben Loaiza-Maya & Didier Nibbering & Dan Zhu, 2023. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Papers 2306.14445, arXiv.org.
    2. Crespo, Marelys & Gadat, Sébastien & Gendre, Xavier, 2023. "Stochastic Langevin Monte Carlo for (weakly) log-concave posterior distributions," TSE Working Papers 23-1398, Toulouse School of Economics (TSE).
    3. Denis Belomestny & Leonid Iosipoi, 2019. "Fourier transform MCMC, heavy tailed distributions and geometric ergodicity," Papers 1909.00698, arXiv.org, revised Dec 2019.
    4. M. Barkhagen & S. García & J. Gondzio & J. Kalcsics & J. Kroeske & S. Sabanis & A. Staal, 2023. "Optimising portfolio diversification and dimensionality," Journal of Global Optimization, Springer, vol. 85(1), pages 185-234, January.
    5. Yang, Jun & Roberts, Gareth O. & Rosenthal, Jeffrey S., 2020. "Optimal scaling of random-walk metropolis algorithms on general target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(10), pages 6094-6132.
    6. Vincent Lemaire & Gilles Pag`es & Christian Yeo, 2023. "Swing contract pricing: with and without Neural Networks," Papers 2306.03822, arXiv.org, revised Mar 2024.
    7. Villeneuve, Stéphane & Bolte, Jérôme & Miclo, Laurent, 2022. "Swarm gradient dynamics for global optimization: the mean-field limit case," TSE Working Papers 22-1302, Toulouse School of Economics (TSE).
    8. Tengyuan Liang & Weijie J. Su, 2019. "Statistical inference for the population landscape via moment‐adjusted stochastic gradients," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 431-456, April.
    9. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
    10. Menz, Georg & Schlichting, André & Tang, Wenpin & Wu, Tianqi, 2022. "Ergodicity of the infinite swapping algorithm at low temperature," Stochastic Processes and their Applications, Elsevier, vol. 151(C), pages 519-552.
    11. Tung Duy Luu & Jalal Fadili & Christophe Chesneau, 2021. "Sampling from Non-smooth Distributions Through Langevin Diffusion," Methodology and Computing in Applied Probability, Springer, vol. 23(4), pages 1173-1201, December.
    12. Gadat, Sébastien & Panloup, Fabien & Pellegrini, C., 2020. "On the cost of Bayesian posterior mean strategy for log-concave models," TSE Working Papers 20-1155, Toulouse School of Economics (TSE), revised Feb 2022.
    13. Brosse, Nicolas & Durmus, Alain & Moulines, Éric & Sabanis, Sotirios, 2019. "The tamed unadjusted Langevin algorithm," Stochastic Processes and their Applications, Elsevier, vol. 129(10), pages 3638-3663.
    14. Belomestny, Denis & Iosipoi, Leonid, 2021. "Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 351-363.
    15. Loaiza-Maya, Rubén & Nibbering, Didier & Zhu, Dan, 2024. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Journal of Econometrics, Elsevier, vol. 241(2).
    16. Chau, Huy N. & Rásonyi, Miklós, 2022. "Stochastic Gradient Hamiltonian Monte Carlo for non-convex learning," Stochastic Processes and their Applications, Elsevier, vol. 149(C), pages 341-368.
    17. Samuel Livingstone & Giacomo Zanella, 2022. "The Barker proposal: Combining robustness and efficiency in gradient‐based MCMC," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 496-523, April.
    18. Arnak Dalalyan, 2017. "Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent," Working Papers 2017-21, Center for Research in Economics and Statistics.

  4. Arnak S. Dalalyan & Mohamed Hebiri & Johannes Lederer, 2014. "On the Prediction Performance of the Lasso," Working Papers 2014-05, Center for Research in Economics and Statistics.

    Cited by:

    1. Pierre Bellec & Alexandre Tsybakov, 2015. "Sharp oracle bounds for monotone and convex regression through aggregation," Working Papers 2015-04, Center for Research in Economics and Statistics.
    2. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
    3. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    4. Jacob Bien & Irina Gaynanova & Johannes Lederer & Christian L. Müller, 2019. "Prediction error bounds for linear regression with the TREX," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 451-474, June.
    5. Gold, David & Lederer, Johannes & Tao, Jing, 2020. "Inference for high-dimensional instrumental variables regression," Journal of Econometrics, Elsevier, vol. 217(1), pages 79-111.
    6. Pawan Gupta & Marianna Pensky, 2018. "Solution of Linear Ill-Posed Problems Using Random Dictionaries," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 178-193, May.
    7. Sheng Xu & Zhou Fan, 2021. "Iterative Alpha Expansion for estimating gradient‐sparse signals from linear measurements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 271-292, April.
    8. Wanling Xie & Hu Yang, 2023. "Group sparse recovery via group square-root elastic net and the iterative multivariate thresholding-based algorithm," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 469-507, September.
    9. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    10. Tung Duy Luu & Jalal Fadili & Christophe Chesneau, 2020. "Sharp oracle inequalities for low-complexity priors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(2), pages 353-397, April.

  5. Olivier Collier & Arnak S, Dalalyan, 2013. "Curve registration by Nonparametric goodness-of-fit Testing," Working Papers 2013-33, Center for Research in Economics and Statistics.

    Cited by:

    1. del Barrio, Eustasio & Gordaliza, Paula & Lescornel, Hélène & Loubes, Jean-Michel, 2019. "Central limit theorem and bootstrap procedure for Wasserstein’s variations with an application to structural relationships between distributions," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 341-362.
    2. Holger Dette & Subhra Sankar Dhar & Weichi Wu, 2021. "Identifying shifts between two regression curves," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(5), pages 855-889, October.
    3. Olivier Collier & Arnak Dalalyan, 2017. "Estimating linear functionals of a sparse family of Poisson means Price Discrimination," Working Papers 2017-19, Center for Research in Economics and Statistics.

  6. Laetitia Comminges & Arnak Dalalyan, 2012. "Minimax Testing of a Composite null Hypothesis Defined via a Quadratic Functional in the Model of regression," Working Papers 2012-19, Center for Research in Economics and Statistics.

    Cited by:

    1. Olivier Collier & Arnak Dalalyan, 2017. "Estimating linear functionals of a sparse family of Poisson means Price Discrimination," Working Papers 2017-19, Center for Research in Economics and Statistics.

  7. Arnak Dalalyan & Yuri Ingster & Alexandre B. Tsybakov, 2012. "Statistical Inference in Compound Functional Models," Working Papers 2012-20, Center for Research in Economics and Statistics.

    Cited by:

    1. Olga Klopp & Marianna Pensky, 2013. "Sparse High-dimensional Varying Coefficient Model : Non-asymptotic Minimax Study," Working Papers 2013-30, Center for Research in Economics and Statistics.

Articles

  1. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
    See citations under working paper version above.
  2. Arnak S. Dalalyan, 2017. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 651-676, June.
    See citations under working paper version above.
  3. Arnak Dalalyan & Yury Kutoyants, 2003. "Asymptotically Efficient Estimation of the Derivative of the Invariant Density," Statistical Inference for Stochastic Processes, Springer, vol. 6(1), pages 89-107, January.

    Cited by:

    1. Dalalyan Arnak S. & Kutoyants Yury A., 2004. "On second order minimax estimation of invariant density for ergodic diffusion," Statistics & Risk Modeling, De Gruyter, vol. 22(1), pages 17-42, January.
    2. Jianqing Fan, 2004. "A selective overview of nonparametric methods in financial econometrics," Papers math/0411034, arXiv.org.
    3. Jianqing Fan & Yingying Fan & Jinchi Lv, 0. "Aggregation of Nonparametric Estimators for Volatility Matrix," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 321-357.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (3) 2012-10-06 2014-08-09 2018-02-12
  2. NEP-FOR: Forecasting (1) 2014-08-09

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