{"id":89,"date":"2015-09-18T18:24:48","date_gmt":"2015-09-18T18:24:48","guid":{"rendered":"http:\/\/lptms.u-psud.fr\/andrey-lokhov\/?page_id=89"},"modified":"2021-04-16T21:40:47","modified_gmt":"2021-04-16T21:40:47","slug":"datacodes","status":"publish","type":"page","link":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/datacodes\/","title":{"rendered":"Data and Codes"},"content":{"rendered":"<p>Open-source implementations of algorithms and models developed by myself\u00a0and close collaborators, as well as datasets used in various research projects.<\/p>\n<p>My page on GitHub: <a href=\"https:\/\/github.com\/lokhov\">https:\/\/github.com\/lokhov<\/a><\/p>\n<ul style=\"list-style-type: circle\">\n<li><em>Julia implementation of RISE, logRISE and RPLE algorithms for the inverse Ising problem<\/em><br \/>\n<a href=\"https:\/\/github.com\/lanl-ansi\/inverse_ising\">https:\/\/github.com\/lanl-ansi\/inverse_ising<\/a><br \/>\n<strong>References:<br \/>\n<\/strong><a href=\"https:\/\/papers.nips.cc\/paper\/6375-interaction-screening-efficient-and-sample-optimal-learning-of-ising-models\">M. Vuffray, S. Misra, A.Y. Lokhov, M. Chertkov,\u00a0\u00ab<em>Interaction screening: efficient and sample-optimal learning of Ising models<\/em>\u00bb, NIPS (2016)<\/a><br \/>\n<a href=\"http:\/\/advances.sciencemag.org\/content\/4\/3\/e1700791\">A.Y. Lokhov, M. Vuffray, S. Misra, M. Chertkov,\u00a0\u00ab<em>Optimal structure and parameter learning of Ising models<\/em>\u00bb, Sci. Adv. (2018)<\/a><\/li>\n<li><em>Julia implementation of Interaction Screening based algorithms for learning general graphical models<br \/>\n<\/em><a href=\"https:\/\/github.com\/lanl-ansi\/GraphicalModelLearning.jl\">https:\/\/github.com\/lanl-ansi\/GraphicalModelLearning.jl<\/a><strong><br \/>\n<\/strong><strong>Reference:<br \/>\n<\/strong><a href=\"https:\/\/arxiv.org\/abs\/1902.00600\">M. Vuffray, S. Misra, A.Y. Lokhov, \u00ab<em>Efficient learning of discrete graphical models<\/em>\u00bb, NeurIPS (2020)<\/a><\/li>\n<li><em>Julia implementation of NeurISE algorithm for learning general graphical models<br \/>\n<\/em><a href=\"https:\/\/github.com\/lanl-ansi\/NeurISE\">https:\/\/github.com\/lanl-ansi\/NeurISE<\/a><br \/>\n<strong>Reference:<br \/>\n<\/strong><a href=\"https:\/\/arxiv.org\/abs\/2006.11937\">Abhijith J., A.Y. Lokhov, S. Misra, M. Vuffray, \u00ab<em>Learning of Discrete Graphical Models with Neural Networks<\/em>\u00bb, NeurIPS (2020)<\/a><\/li>\n<li><em>Julia implementation of Dynamic Message-Passing algorithm for inference and learning for the Independent Cascade model<br \/>\n<\/em><a href=\"https:\/\/github.com\/mateuszwilinski\/dynamic-message-passing\/\">https:\/\/github.com\/mateuszwilinski\/dynamic-message-passing\/<\/a><strong><br \/>\n<\/strong><strong>Reference:<br \/>\n<\/strong><a href=\"https:\/\/arxiv.org\/abs\/2007.06557\">M. Wilinski, A.Y. Lokhov, \u00ab<em>Scalable Learning of Independent Cascade Dynamics from Partial Observations<\/em>\u00bb, arXiv (2020)<\/a><\/li>\n<li><em>R implementation of anomaly detection from multivariate time series<br \/>\n<\/em><a href=\"https:\/\/github.com\/lanl-ansi\/MVAD\">https:\/\/github.com\/lanl-ansi\/MVAD<\/a><strong><br \/>\n<\/strong><strong>Reference:<br \/>\n<\/strong><a href=\"https:\/\/arxiv.org\/abs\/1911.06316\">C. Hannon et al., \u00ab<em>Real-time Anomaly Detection and Classification in Streaming PMU Data<\/em>\u00bb, PowerTech (2021)<\/a><\/li>\n<li><em>Julia implementation of the Quantum Annealing Single-qubit Assessment protocol<br \/>\n<\/em><a href=\"https:\/\/github.com\/lanl-ansi\/QASA\">https:\/\/github.com\/lanl-ansi\/QASA<\/a><strong><br \/>\n<\/strong><strong>Reference:<br \/>\n<\/strong><a href=\"https:\/\/arxiv.org\/abs\/2104.03335\">J. Nelson, M. Vuffray, A. Y. Lokhov, C. Coffrin, \u00ab<em>Single-Qubit Fidelity Assessment of Quantum Annealing Hardware<\/em>\u00bb, arXiv (2021)\u00a0<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Open-source implementations of algorithms and models developed by myself\u00a0and close collaborators, as well as datasets used in various research projects. My page on GitHub: https:\/\/github.com\/lokhov Julia implementation of RISE, logRISE and RPLE algorithms for the inverse Ising problem https:\/\/github.com\/lanl-ansi\/inverse_ising References: &hellip; <a href=\"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/datacodes\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":28,"featured_media":0,"parent":0,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-89","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/pages\/89","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/comments?post=89"}],"version-history":[{"count":23,"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/pages\/89\/revisions"}],"predecessor-version":[{"id":607,"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/pages\/89\/revisions\/607"}],"wp:attachment":[{"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/media?parent=89"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}