{"id":37,"date":"2013-12-09T00:14:25","date_gmt":"2013-12-09T00:14:25","guid":{"rendered":"http:\/\/lptms.u-psud.fr\/andrey-lokhov\/?page_id=37"},"modified":"2022-10-12T03:10:51","modified_gmt":"2022-10-12T03:10:51","slug":"research","status":"publish","type":"page","link":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

Statistical Learning and Graphical Models<\/h3>\n\n\n\n\n\n\n\n\n\n
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Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
\nLearning Continuous Exponential Families Beyond Gaussian<\/strong>
\nSubmitted [ArXiv<\/a>]<\/p>\n

Short description: Introducing a new estimator ISODUS for\u00a0continuous non-Gaussian exponential family distributions with unbounded support and multi-body interactions<\/em><\/p>\n

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Arkopal Dutt,\u00a0Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra,
\nExponential Reduction in Sample Complexity with Learning of Ising Model Dynamics<\/strong>
\nInternational Conference on Machine Learning (ICML 2021)<\/a> [ArXiv<\/a>]<\/p>\n

Short description: Learning of graphical models from correlated samples in the out-of-equilibrium regime is exponentially easier compared to the independent samples setting<\/em><\/p>\n

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Abhijith J., Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray
\nLearning of discrete graphical models with neural networks<\/strong>
\nAdvances in Neural Information Processing Systems (NeurIPS 2020)<\/a> [ArXiv<\/a>]<\/p>\n

Short description: Discovering parsimonious\u00a0basis representation with NeurISE, an Interaction Screening based estimator incorporating neural networks acting as universal energy function approximators<\/em><\/p>\n

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Marc Vuffray, Sidhant Misra,\u00a0Andrey Y. Lokhov
\nEfficient learning of discrete graphical models<\/strong>
\nAdvances in Neural Information Processing Systems (NeurIPS 2020)<\/a> [ArXiv<\/a>]<\/p>\n

Short description: Learning discrete graphical models with\u00a0<\/em>arbitrary alphabets and multi-body interactions using GRISE, Generalized Regularized Interaction Screening Estimator<\/em><\/p>\n

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Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
\nInformation theoretic optimal learning of Gaussian graphical models<\/strong>
\nConference on Learning Theory (COLT 2020)<\/a>\u00a0[pdf<\/a>] [ArXiv<\/a>]<\/p>\n

Short description: Beyond LASSO with SLICE and DICE algorithms that achieve the IT bound on sample complexity for learning the structure of Gaussian graphical models<\/em><\/td>\n<\/tr>\n

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\"Ising-1\"<\/a><\/strong><\/p>\n

Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov
\nOptimal structure and parameter learning of Ising models
\n<\/em><\/strong>Science Advances, 4 , e1700791 (2018)<\/a> [pdf<\/a>] [ArXiv<\/a>] [Code<\/a>]<\/p>\n

Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov
\nInteraction screening: efficient and sample-optimal learning of Ising models<\/strong>
\n<\/em>Advances in Neural Information Processing Systems (NIPS)\u00a02016<\/a> [pdf<\/a>] [ArXiv<\/a>]<\/p>\n

Short description: Sample-optimal \u201cInteraction Screening\u201d method for provably learning arbitrary binary graphical models without any assumptions<\/em><\/p>\n<\/td>\n<\/tr>\n

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Andrey Y. Lokhov, Olga V. Valba, Mikhail V. Tamm, Sergei K. Nechaev
\nPhase transition in random planar diagrams and RNA-type matching<\/strong>
\nPhys. Rev. E 88, 052117 (2013)<\/a> [pdf<\/a>] [ArXiv<\/a>]<\/p>\n

Andrey Y. Lokhov, Olga V. Valba, Sergei K. Nechaev, Mikhail V. Tamm
\nTopological transition in disordered planar matching: combinatorial arcs expansion
\n<\/strong>J. Stat. Mech. P12004 (2014)<\/a> [<\/span>pdf<\/a>] [<\/span>ArXiv<\/a>]<\/span><\/p>\n

Short description: Combinatorics of RNA-type matching structures and new phase transition<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Dynamic Message-Passing and Spreading Processes<\/h3>\n\n\n\n\n\n\n\n\n\n\n
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Mateusz Wilinski,\u00a0Lauren Castro, Jeffrey Keithley, Carrie Manore, Josefina Campos, Ethan Romero-Severson, Daryl Domman, Andrey Y. Lokhov
\nCongruity of genomic and epidemiological data in modeling of local cholera outbreaks<\/strong>
\nSubmitted [ArXiv<\/a>]<\/p>\n

Short description: We use high-fidelity case count and whole genome sequencing data from the 1991-1998 cholera epidemic in Argentina, and show that consistency between the epidemiological model parameters estimated from both genetic and case-count data sources.<\/em><\/td>\n<\/tr>\n

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Mateusz Wilinski, Andrey Y. Lokhov
\nScalable Learning of Independent Cascade Dynamics from Partial Observations<\/strong>
\nInternational Conference on Machine Learning (ICML 2021)<\/a> [ArXiv<\/a>]<\/p>\n

Short description: Introducing a scalable algorithm SLICER that estimates parameters of the Independent Cascade model. In the context of learning for inference, tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model<\/em><\/td>\n<\/tr>\n

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Andrey Y. Lokhov, David Saad
\nScalable Influence Estimation Without Sampling<\/strong>
\nSubmitted [ArXiv<\/a>]<\/p>\n

Short description: Scalable dynamic message-passing algorithm for estimation of spread in the Independent Cascade type diffusion models<\/em><\/td>\n<\/tr>\n

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Hanlin Sun, David Saad,\u00a0Andrey Y. Lokhov
\nCompetition, Collaboration, and Optimization in Multiple Interacting Spreading Processes<\/strong>
\nPhys. Rev. X 11, 011048 (2021)<\/a>\u00a0[pdf<\/a>] [ArXiv<\/a>]<\/p>\n

Short description: Exact dynamic message-passing equations for estimation of marginal infection probabilities for collaborative and mutually exclusive epidemics, and their use for the optimal resource allocation problem<\/em><\/td>\n<\/tr>\n

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Andrey Y. Lokhov, David Saad
\nOptimal deployment of resources for maximizing impact in spreading processes<\/strong>
\nProceedings of the National Academy of Sciences, 114 (39) E8138-E8146 (2017)<\/a> [pdf<\/a>] [ArXiv<\/a>]<\/p>\n

Short description: Optimal targeting in spreading processes with\u00a0dynamic message-passing equations and forward-backward optimization method used in\u00a0artificial neural networks<\/em><\/td>\n<\/tr>\n

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Andrey Y. Lokhov
\nReconstructing parameters of spreading models from partial observations
\n<\/em><\/strong>Advances in Neural Information Processing Systems (NIPS)\u00a02016<\/a> [pdf<\/a>] [ArXiv<\/a>] [video<\/a>]<\/p>\n

Andrey Y. Lokhov,\u00a0Theodor Misiakiewicz
\nEfficient reconstruction of transmission probabilities in a spreading process from partial observations
\n<\/strong>Work in progress [ArXiv<\/a>]<\/p>\n

Short description: Introducing a dynamic message-passing algorithm DMPrec for learning parameters of spreading models from partial observations\u00a0<\/em><\/td>\n<\/tr>\n

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Andrey Y. Lokhov, Marc M\u00e9zard, Lenka Zdeborov\u00e1
\nDynamic message-passing equations for models with unidirectional dynamics<\/strong>
\nPhys. Rev. E 91, 012811 (2015)<\/a> [pdf<\/a>] [ArXiv<\/a>]<\/p>\n

Short description: Solution of many dynamic models (random field Ising model, epidemic and rumor spreading, threshold models) on given network instances<\/em><\/td>\n<\/tr>\n

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Andrey Y. Lokhov, Marc M\u00e9zard, Hiroki Ohta, Lenka Zdeborov\u00e1
\nInferring the origin of an epidemic with a dynamic message-passing algorithm<\/strong>
\nPhys. Rev. E 90, 012801 (2014)<\/a>\u00a0 [pdf<\/a>] [ArXiv<\/a>]<\/p>\n

Short description: Localization of the epidemic source from a partial snapshot at unknown time<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Quantum Computing<\/h3>\n\n\n\n\n\n\n\n\n\n\n
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Byron Tasseff et al.<\/em>
\nOn the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization<\/strong>
\nSubmitted [ArXiv<\/a>]<\/p>\n

Short description: We demonstrate the existence of classes of contrived optimization problems where\u00a0<\/em>D-Wave Systems’ most recent Advantage Performance Update quantum annealer provides run time benefits over a collection of established classical solution methods.<\/em><\/td>\n<\/tr>\n

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Zachary Morrell\u00a0et al.<\/em>
\nSignatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing
\n<\/strong>Submitted\u00a0[ArXiv<\/a>]<\/p>\n

Short description: This paper shows that both thermal and magnetic field fluctuations are key sources of noise that need to be included in an open quantum system model to reproduce the output statistics of the hardware.<\/em><\/td>\n<\/tr>\n

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Abhijith J. et al.<\/em>
\nQuantum Algorithm Implementations for Beginners<\/strong>
\nACM Transactions on Quantum Computing, Volume 3, Issue 4, 18, pp. 1\u201392 (2022)<\/a> [ArXiv<\/a>]<\/p>\n

Short description: An introduction to quantum computing algorithms and their implementation on IBM QX quantum computer<\/em><\/p>\n

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Adrien Suau, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin
\nVector Field Visualization of Single-Qubit State Tomography<\/strong>
\nAccepted to\u00a0IEEE QCE 2022\u00a0[ArXiv<\/a>]<\/p>\n

Short description:\u00a0Developing a vector field visualization for\u00a0<\/em>quantum state tomography characterization of individual qubits, and demonstration of qubit performance features in IBM quantum computing hardware.<\/em><\/td>\n<\/tr>\n

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Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Tameem Albash, and Carleton Coffrin
\n<\/strong>High-Quality Thermal Gibbs Sampling with Quantum Annealing Hardware
\n<\/strong>Phys. Rev. Applied 17, 044046 (2022)<\/a> [ArXiv<\/a>]
\n<\/strong><\/p>\n

Short description: Introducing a procedure for producing high-quality samples from quantum annealing hardware<\/em><\/td>\n<\/tr>\n

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Marc Vuffray, Carleton Coffrin, Yaroslav Kharkov, Andrey Y. Lokhov
\nProgrammable Quantum Annealers as Noisy Gibbs Samplers
\n<\/strong>PRX Quantum 3, 020317 (2022)<\/a> [ArXiv<\/a>]
\n<\/strong><\/p>\n

Short description: Characterization of quantum annealers’ sampling properties using statistical learning methods, including unexpected spurious interactions in the output distribution due to qubit noise<\/em><\/td>\n<\/tr>\n

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Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Carleton Coffrin
\nSingle-Qubit Fidelity Assessment of Quantum Annealing Hardware
\n<\/strong>IEEE Transactions on Quantum Engineering, 2, 1-10 (2021)<\/a> [ArXiv<\/a>]
\n<\/strong><\/p>\n

Adrien Suau et al.<\/em>
\nSingle-Qubit Cross Platform Comparison of Quantum Computing Hardware<\/strong>
\nSubmitted [ArXiv<\/a>]<\/p>\n

Short description: Quantifying the error performance of individual qubits in quantum annealing and gate quantum computers<\/em><\/p>\n

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\nThe Potential of Quantum Annealing for Rapid Solution Structure Identification
\n<\/strong>Yuchen Pang, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray
\nConstraints (2020)<\/a> [ArXiv<\/a>]
\nPresented at\u00a0CPAIOR 2020<\/a><\/p>\n

Short description: Identification of a hard instance of an optimization problem where quantum annealing provides notable performance gains over established classical algorithms<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Dynamical Systems, Power Grid, and Cyber-Physical Systems<\/h3>\n\n\n\n\n\n\n\n\n
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Zheguang Zhao, Deepjyoti Deka, Andrey Y. Lokhov
\nLearning of Cyber-Physical Systems
\n<\/strong>Work in progress
\n<\/strong><\/p>\n

Short description:\u00a0Learning of an effective cyber-physical model from discrete and continuous time series of physical and control processes<\/em><\/p>\n<\/td>\n<\/tr>\n

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Jordan Snyder, Anatoly Zlotnik, Andrey Y. Lokhov
\nData-driven Selection of Coarse-Grained Models of Coupled Oscillators
\n<\/strong>Phys. Rev. Research 2, 043402 (2020)<\/a>\u00a0[pdf<\/a>] [ArXiv<\/a>]
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Short description: Learning of macroscopic reduced-order models in systems of coupled oscillators from coarse-grained microscopic data<\/em><\/p>\n

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Bo Li, David Saad,\u00a0Andrey Y. Lokhov
\nReducing Urban Traffic Congestion Due To Localized Routing Decisions
\n<\/strong>Phys. Rev. Research 2, 032059(R) (2020)<\/a> [pdf<\/a>] [<\/span>ArXiv<\/a>]<\/span><\/p>\n

Short description: Discovery of paradoxical traffic patterns emerging within a new traffic model that includes\u00a0<\/em>localized routing inducement, and development of a scalable optimization algorithm for identifying mechanisms to minimize congestion<\/em><\/p>\n

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Christopher Hannon, Deepjyoti Deka, Dong Jin, Marc Vuffray, Andrey Y. Lokhov
\nReal-time Anomaly Detection and Classification in Streaming PMU Data
\n<\/strong>IEEE PowerTech 2021<\/a> [ArXiv<\/a>]
\nPresented in a demo track of NeurIPS 2019<\/a><\/p>\n

Short description: Anomaly detection and classification in streaming phasor measurement units data via real-time learning of effective dynamical model<\/em><\/p>\n<\/td>\n<\/tr>\n

\"\"<\/a>Andrey Y. Lokhov, Marc Vuffray, Dmitry Shemetov, Deepjyoti Deka, Michael Chertkov
\nOnline Learning of Power Transmission Dynamics<\/strong>
\nPSCC 2018<\/a> [ArXiv<\/a>]Andrey Y. Lokhov, Deepjyoti Deka, Marc Vuffray, Michael Chertkov
\nUncovering Power Transmission Dynamic Model from Incomplete PMU Observations
\n<\/strong>CDC 2018<\/a>Deepjyoti Deka, Armin Zare,\u00a0 Andrey Y. Lokhov, Mihailo Jovanovic, Michael Chertkov
\nEstimation of state and noise covariance in power grids using limited nodal PMUs
\n<\/strong>IEEE Global Conference on Signal and Information Processing (2017)<\/a> [pdf<\/a>]<\/td>\n<\/tr>\n
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Andrey Y. Lokhov, Nathan Lemons, Thomas C. McAndrew, Aric Hagberg, Scott Backhaus
\nDetection of cyber-physical faults and intrusions from physical correlations
\n<\/em><\/strong>IEEE 16th International Conference on Data Mining Workshops (ICDMW),\u00a0303-310 (2016)<\/a>
\n[pdf<\/a>] [ArXiv<\/a>]
\nPresented at “Outlier Definition, Detection, and Description on Demand” workshop<\/a> at KDD 2016<\/p>\n

Short description: Detection of anomalies in cyber-physical systems from real data<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/h3>\n","protected":false},"excerpt":{"rendered":"

Statistical Learning and Graphical Models   Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov Learning Continuous Exponential Families Beyond Gaussian Submitted [ArXiv] Short description: Introducing a new estimator ISODUS for\u00a0continuous non-Gaussian exponential family distributions with unbounded support and … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":28,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-37","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/pages\/37","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=37"}],"version-history":[{"count":245,"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/pages\/37\/revisions"}],"predecessor-version":[{"id":682,"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/pages\/37\/revisions\/682"}],"wp:attachment":[{"href":"http:\/\/www.lptms.universite-paris-saclay.fr\/andrey-lokhov\/wp-json\/wp\/v2\/media?parent=37"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}