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ORCIDAndrey Y. Lokhov
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2020 – today
- 2024
[j4]Byron Tasseff, Tameem Albash, Zachary Morrell, Marc Vuffray, Andrey Y. Lokhov, Sidhant Misra, Carleton Coffrin
:
On the emerging potential of quantum annealing hardware for combinatorial optimization. J. Heuristics 30(5): 325-358 (2024)- [c15]Andrew Chio, Russell Bent, Andrey Y. Lokhov, Jian Peng, Nalini Venkatasubramanian:
Physics-based Pollutant Source Identification in Stormwater Systems. ECC 2024: 19-25 - [i24]Mateusz Wilinski, Andrey Y. Lokhov:
Learning of networked spreading models from noisy and incomplete data. CoRR abs/2401.00011 (2024) - 2023
- [c14]Abhijith Jayakumar, Stefano Chessa, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra:
General Algorithms for SPAM Noise Characterization. QCE 2023: 407-408 - [c13]Adrien Suau, Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin:
Single-Qubit Cross Platform Comparison of Quantum Computing Hardware. QCE 2023: 1369-1377 - [i23]Abhijith Jayakumar, Marc Vuffray, Andrey Y. Lokhov:
Learning Energy-Based Representations of Quantum Many-Body States. CoRR abs/2304.04058 (2023) - [i22]Melvyn Tyloo, Marc Vuffray, Andrey Y. Lokhov:
Forced oscillation source localization from generator measurements. CoRR abs/2310.00458 (2023) - 2022
- [c12]Adrien Suau, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin:
Vector Field Visualization of Single-Qubit State Tomography. QCE 2022: 528-534 - 2021
- [j3]Yuchen Pang, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray:
The potential of quantum annealing for rapid solution structure identification. Constraints An Int. J. 26(1): 1-25 (2021) - [j2]Yuchen Pang, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray:
Correction to: The potential of quantum annealing for rapid solution structure identification. Constraints An Int. J. 26(1): 107 (2021) - [c11]Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra:
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics. ICML 2021: 2914-2925 - [c10]Mateusz Wilinski, Andrey Y. Lokhov:
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations. ICML 2021: 11182-11192 - [i21]Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov:
Learning Continuous Exponential Families Beyond Gaussian. CoRR abs/2102.09198 (2021) - [i20]Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra:
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics. CoRR abs/2104.00995 (2021) - [i19]Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Carleton Coffrin:
Single-Qubit Fidelity Assessment of Quantum Annealing Hardware. CoRR abs/2104.03335 (2021) - [i18]Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Tameem Albash, Carleton Coffrin:
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware. CoRR abs/2109.01690 (2021) - 2020
- [c9]Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov:
Information Theoretic Optimal Learning of Gaussian Graphical Models. COLT 2020: 2888-2909 - [c8]Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray:
Learning of Discrete Graphical Models with Neural Networks. NeurIPS 2020 - [c7]Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov:
Efficient Learning of Discrete Graphical Models. NeurIPS 2020 - [i17]Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray:
Learning of Discrete Graphical Models with Neural Networks. CoRR abs/2006.11937 (2020) - [i16]Mateusz Wilinski, Andrey Y. Lokhov:
Scalable Learning of Independent Cascade Dynamics from Partial Observations. CoRR abs/2007.06557 (2020) - [i15]Risul Islam, Andrey Y. Lokhov, Nathan Lemons, Michalis Faloutsos:
Mobility Map Inference from Thermal Modeling of a Building. CoRR abs/2011.07372 (2020) - [i14]Marc Vuffray, Carleton Coffrin, Yaroslav A. Kharkov, Andrey Y. Lokhov:
Programmable Quantum Annealers as Noisy Gibbs Samplers. CoRR abs/2012.08827 (2020)
2010 – 2019
- 2019
- [i13]Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov:
Efficient Learning of Discrete Graphical Models. CoRR abs/1902.00600 (2019) - [i12]Andrey Y. Lokhov, David Saad:
Scalable Influence Estimation Without Sampling. CoRR abs/1912.12749 (2019) - 2018
- [c6]Andrey Y. Lokhov, Deepjyoti Deka, Marc Vuffray, Michael Chertkov:
Uncovering Power Transmission Dynamic Model from Incomplete PMU Observations. CDC 2018: 4008-4013 - [i11]Patrick J. Coles, Stephan J. Eidenbenz, Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr M. Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo N. Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Y. Lokhov, Alexander Malyzhenkov, David Dennis Lee Mascarenas, Susan M. Mniszewski, Balu Nadiga, Dan O'Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Philip Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard J. Zamora, Wei Zhu:
Quantum Algorithm Implementations for Beginners. CoRR abs/1804.03719 (2018) - 2017
- [j1]Andrey Y. Lokhov, David Saad:
Optimal deployment of resources for maximizing impact in spreading processes. Proc. Natl. Acad. Sci. USA 114(39): E8138-E8146 (2017) - [c5]Deepjyoti Deka, Armin Zare, Andrey Y. Lokhov, Mihailo R. Jovanovic, Michael Chertkov:
State and noise covariance estimation in power grids using limited nodal PMUs. GlobalSIP 2017: 1075-1079 - [c4]Emma M. Stewart, Philip Top, Michael Chertkov, Deepjyoti Deka, Scott Backhaus, Andrey Y. Lokhov, Ciaran M. Roberts, Val Hendrix, Sean Peisert, Anthony Florita, Thomas J. King, Matthew J. Reno:
Integrated multi-scale data analytics and machine learning for the distribution grid. SmartGridComm 2017: 423-429 - [i10]Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov, Michael Chertkov:
Towards Optimal Sparse Inverse Covariance Selection through Non-Convex Optimization. CoRR abs/1703.04886 (2017) - [i9]Andrey Y. Lokhov, Marc Vuffray, Dmitry Shemetov, Deepjyoti Deka, Michael Chertkov:
Online Learning of Power Transmission Dynamics. CoRR abs/1710.10021 (2017) - 2016
- [c3]Andrey Y. Lokhov, Nathan Lemons, Thomas C. McAndrew, Aric A. Hagberg, Scott Backhaus:
Detection of Cyber-Physical Faults and Intrusions from Physical Correlations. ICDM Workshops 2016: 303-310 - [c2]Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov:
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models. NIPS 2016: 2595-2603 - [c1]Andrey Y. Lokhov:
Reconstructing Parameters of Spreading Models from Partial Observations. NIPS 2016: 3459-3467 - [i8]Andrey Y. Lokhov, Nathan Lemons, Thomas C. McAndrew, Aric A. Hagberg, Scott Backhaus:
Detection of faults and intrusions in cyber-physical systems from physical correlations. CoRR abs/1602.06604 (2016) - [i7]Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov:
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models. CoRR abs/1605.07252 (2016) - [i6]Andrey Y. Lokhov, David Saad:
Optimal Deployment of Resources for Maximizing Impact in Spreading Processes. CoRR abs/1608.08278 (2016) - [i5]Andrey Y. Lokhov:
Reconstructing parameters of spreading models from partial observations. CoRR abs/1608.08698 (2016) - [i4]Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov:
Optimal structure and parameter learning of Ising models. CoRR abs/1612.05024 (2016) - 2015
- [i3]Andrey Y. Lokhov, Theodor Misiakiewicz:
Efficient reconstruction of transmission probabilities in a spreading process from partial observations. CoRR abs/1509.06893 (2015) - 2014
- [i2]Andrey Y. Lokhov, Marc Mézard, Lenka Zdeborová:
Dynamic message-passing equations for models with unidirectional dynamics. CoRR abs/1407.1255 (2014) - 2013
- [i1]Andrey Y. Lokhov, Marc Mézard, Hiroki Ohta, Lenka Zdeborová:
Inferring the origin of an epidemy with dynamic message-passing algorithm. CoRR abs/1303.5315 (2013)
Coauthor Index
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