Statistical Learning and Graphical Models
Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov Short description: Introducing a new estimator ISODUS for continuous non-Gaussian exponential family distributions with unbounded support and multi-body interactions
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Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Short description: Learning of graphical models from correlated samples in the out-of-equilibrium regime is exponentially easier compared to the independent samples setting
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Abhijith J., Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray Short description: Discovering parsimonious basis representation with NeurISE, an Interaction Screening based estimator incorporating neural networks acting as universal energy function approximators
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Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov Short description: Learning discrete graphical models with arbitrary alphabets and multi-body interactions using GRISE, Generalized Regularized Interaction Screening Estimator
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Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov 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 |
Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov Short description: Sample-optimal “Interaction Screening” method for provably learning arbitrary binary graphical models without any assumptions |
![]() Andrey Y. Lokhov, Olga V. Valba, Mikhail V. Tamm, Sergei K. Nechaev Andrey Y. Lokhov, Olga V. Valba, Sergei K. Nechaev, Mikhail V. Tamm Short description: Combinatorics of RNA-type matching structures and new phase transition |
Dynamic Message-Passing and Spreading Processes
![]() Mateusz Wilinski, Lauren Castro, Jeffrey Keithley, Carrie Manore, Josefina Campos, Ethan Romero-Severson, Daryl Domman, Andrey Y. Lokhov 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. |
![]() Mateusz Wilinski, Andrey Y. Lokhov 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 |
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Andrey Y. Lokhov, David Saad Short description: Scalable dynamic message-passing algorithm for estimation of spread in the Independent Cascade type diffusion models |
![]() Hanlin Sun, David Saad, Andrey Y. Lokhov 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 |
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Andrey Y. Lokhov, David Saad Short description: Optimal targeting in spreading processes with dynamic message-passing equations and forward-backward optimization method used in artificial neural networks |
![]() Andrey Y. Lokhov Andrey Y. Lokhov, Theodor Misiakiewicz Short description: Introducing a dynamic message-passing algorithm DMPrec for learning parameters of spreading models from partial observations |
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Andrey Y. Lokhov, Marc Mézard, Lenka Zdeborová Short description: Solution of many dynamic models (random field Ising model, epidemic and rumor spreading, threshold models) on given network instances |
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Andrey Y. Lokhov, Marc Mézard, Hiroki Ohta, Lenka Zdeborová Short description: Localization of the epidemic source from a partial snapshot at unknown time |
Quantum Computing
![]() Byron Tasseff et al. Short description: We demonstrate the existence of classes of contrived optimization problems where D-Wave Systems’ most recent Advantage Performance Update quantum annealer provides run time benefits over a collection of established classical solution methods. |
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Zachary Morrell et al. 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. |
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Abhijith J. et al. Short description: An introduction to quantum computing algorithms and their implementation on IBM QX quantum computer
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Adrien Suau, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin Short description: Developing a vector field visualization for quantum state tomography characterization of individual qubits, and demonstration of qubit performance features in IBM quantum computing hardware. |
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Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Tameem Albash, and Carleton Coffrin Short description: Introducing a procedure for producing high-quality samples from quantum annealing hardware |
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Marc Vuffray, Carleton Coffrin, Yaroslav Kharkov, Andrey Y. Lokhov Short description: Characterization of quantum annealers’ sampling properties using statistical learning methods, including unexpected spurious interactions in the output distribution due to qubit noise |
![]() Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Carleton Coffrin Adrien Suau et al. Short description: Quantifying the error performance of individual qubits in quantum annealing and gate quantum computers
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Short description: Identification of a hard instance of an optimization problem where quantum annealing provides notable performance gains over established classical algorithms |
Dynamical Systems, Power Grid, and Cyber-Physical Systems
Zheguang Zhao, Deepjyoti Deka, Andrey Y. Lokhov Short description: Learning of an effective cyber-physical model from discrete and continuous time series of physical and control processes |
Jordan Snyder, Anatoly Zlotnik, Andrey Y. Lokhov Short description: Learning of macroscopic reduced-order models in systems of coupled oscillators from coarse-grained microscopic data
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Bo Li, David Saad, Andrey Y. Lokhov Short description: Discovery of paradoxical traffic patterns emerging within a new traffic model that includes localized routing inducement, and development of a scalable optimization algorithm for identifying mechanisms to minimize congestion
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Christopher Hannon, Deepjyoti Deka, Dong Jin, Marc Vuffray, Andrey Y. Lokhov Short description: Anomaly detection and classification in streaming phasor measurement units data via real-time learning of effective dynamical model |
![]() Online Learning of Power Transmission Dynamics PSCC 2018 [ArXiv]Andrey Y. Lokhov, Deepjyoti Deka, Marc Vuffray, Michael Chertkov Uncovering Power Transmission Dynamic Model from Incomplete PMU Observations CDC 2018Deepjyoti Deka, Armin Zare, Andrey Y. Lokhov, Mihailo Jovanovic, Michael Chertkov Estimation of state and noise covariance in power grids using limited nodal PMUs IEEE Global Conference on Signal and Information Processing (2017) [pdf] |
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Andrey Y. Lokhov, Nathan Lemons, Thomas C. McAndrew, Aric Hagberg, Scott Backhaus Short description: Detection of anomalies in cyber-physical systems from real data |