Andrey Y. Lokhov

According to our database, Andrey Y. Lokhov authored at least 38 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

On the emerging potential of quantum annealing hardware for combinatorial optimization.

Byron Tasseff, Tameem Albash, Zachary Morrell, Marc Vuffray, Andrey Y. Lokhov, Sidhant Misra, Carleton Coffrin

J. Heuristics, December, 2024

Discrete distributions are learnable from metastable samples.

Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray

CoRR, 2024

Learning of networked spreading models from noisy and incomplete data.

Mateusz Wilinski, Andrey Y. Lokhov

CoRR, 2024

Physics-based Pollutant Source Identification in Stormwater Systems.

Andrew Chio, Russell Bent, Andrey Y. Lokhov, Jian Peng, Nalini Venkatasubramanian

Proceedings of the European Control Conference, 2024

2023

Forced oscillation source localization from generator measurements.

Melvyn Tyloo, Marc Vuffray, Andrey Y. Lokhov

CoRR, 2023

Learning Energy-Based Representations of Quantum Many-Body States.

Abhijith Jayakumar, Marc Vuffray, Andrey Y. Lokhov

CoRR, 2023

Single-Qubit Cross Platform Comparison of Quantum Computing Hardware.

Adrien Suau, Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

General Algorithms for SPAM Noise Characterization.

Abhijith Jayakumar, Stefano Chessa, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

2022

Vector Field Visualization of Single-Qubit State Tomography.

Adrien Suau, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2022

2021

High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware.

Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Tameem Albash, Carleton Coffrin

CoRR, 2021

Single-Qubit Fidelity Assessment of Quantum Annealing Hardware.

Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Carleton Coffrin

CoRR, 2021

Learning Continuous Exponential Families Beyond Gaussian.

Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov

CoRR, 2021

Correction to: The potential of quantum annealing for rapid solution structure identification.

Yuchen Pang, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray

Constraints An Int. J., 2021

The potential of quantum annealing for rapid solution structure identification.

Yuchen Pang, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray

Constraints An Int. J., 2021

Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations.

Mateusz Wilinski, Andrey Y. Lokhov

Proceedings of the 38th International Conference on Machine Learning, 2021

Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics.

Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Programmable Quantum Annealers as Noisy Gibbs Samplers.

Marc Vuffray, Carleton Coffrin, Yaroslav A. Kharkov, Andrey Y. Lokhov

CoRR, 2020

Mobility Map Inference from Thermal Modeling of a Building.

Risul Islam, Andrey Y. Lokhov, Nathan Lemons, Michalis Faloutsos

CoRR, 2020

Scalable Learning of Independent Cascade Dynamics from Partial Observations.

Mateusz Wilinski, Andrey Y. Lokhov

CoRR, 2020

Efficient Learning of Discrete Graphical Models.

Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning of Discrete Graphical Models with Neural Networks.

Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Information Theoretic Optimal Learning of Gaussian Graphical Models.

Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov

Proceedings of the Conference on Learning Theory, 2020

2019

Scalable Influence Estimation Without Sampling.

Andrey Y. Lokhov, David Saad

CoRR, 2019

2018

Quantum Algorithm Implementations for Beginners.

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

CoRR, 2018

Uncovering Power Transmission Dynamic Model from Incomplete PMU Observations.

Andrey Y. Lokhov, Deepjyoti Deka, Marc Vuffray, Michael Chertkov

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Optimal deployment of resources for maximizing impact in spreading processes.

Andrey Y. Lokhov, David Saad

Proc. Natl. Acad. Sci. USA, 2017

Online Learning of Power Transmission Dynamics.

Andrey Y. Lokhov, Marc Vuffray, Dmitry Shemetov, Deepjyoti Deka, Michael Chertkov

CoRR, 2017

Towards Optimal Sparse Inverse Covariance Selection through Non-Convex Optimization.

Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov, Michael Chertkov

CoRR, 2017

Integrated multi-scale data analytics and machine learning for the distribution grid.

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

Proceedings of the 2017 IEEE International Conference on Smart Grid Communications, 2017

State and noise covariance estimation in power grids using limited nodal PMUs.

Deepjyoti Deka, Armin Zare, Andrey Y. Lokhov, Mihailo R. Jovanovic, Michael Chertkov

Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

2016

Optimal structure and parameter learning of Ising models.

Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov

CoRR, 2016

Detection of faults and intrusions in cyber-physical systems from physical correlations.

Andrey Y. Lokhov, Nathan Lemons, Thomas C. McAndrew, Aric A. Hagberg, Scott Backhaus

CoRR, 2016

Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models.

Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Reconstructing Parameters of Spreading Models from Partial Observations.

Andrey Y. Lokhov

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Detection of Cyber-Physical Faults and Intrusions from Physical Correlations.

Andrey Y. Lokhov, Nathan Lemons, Thomas C. McAndrew, Aric A. Hagberg, Scott Backhaus

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

2015

Efficient reconstruction of transmission probabilities in a spreading process from partial observations.

Andrey Y. Lokhov, Theodor Misiakiewicz

CoRR, 2015

2014

Dynamic message-passing equations for models with unidirectional dynamics.

Andrey Y. Lokhov, Marc Mézard, Lenka Zdeborová

CoRR, 2014

2013

Inferring the origin of an epidemy with dynamic message-passing algorithm

Andrey Y. Lokhov, Marc Mézard, Hiroki Ohta, Lenka Zdeborová

CoRR, 2013