Selected Publications

Machine Learning

  • Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
    A. Dutt, A.Y. Lokhov, M. Vuffray, S. Misra,
    International Conference on Machine Learning (ICML), 2021, [online], [arXiv]

  • Learning of Discrete Graphical Models with Neural Networks
    Abhijith J., A.Y. Lokhov, S. Misra, M. Vuffray
    Advances in Neural Information Processing Systems (NeurIPS), 2020, [online], [arXiv]

  • Efficient Learning of Discrete Graphical Models
    M. Vuffray, S. Misra, A.Y. Lokhov
    Advances in Neural Information Processing Systems (NeurIPS), 2020, [online], [arXiv]

  • Information Theoretic Optimal Learning of Gaussian Graphical Models
    S. Misra, M. Vuffray, A.Y. Lokhov
    Conference on Learning Theory (COLT), 2020, [online], [arXiv]

  • Optimal Structure and Parameter Learning of Ising Models
    A.Y. Lokhov, M. Vuffray, S. Misra, M. Chertkov
    Science Advances, 2018, [online], [arXiv]

  • Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
    M. Vuffray, S. Misra, A.Y. Lokhov, M. Chertkov
    Advances in Neural Information Processing Systems (NeurIPS), 2016, [online], [arXiv]

Quantum Computing

  • High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
    J. Nelson, M. Vuffray, A.Y. Lokhov, T. Albash, C. Coffrin
    arXiv, 2021, [arXiv]

  • Single-Qubit Cross Platform Comparison of Quantum Computing Hardware
    A. Suau, J. Nelson, M. Vuffray, A.Y. Lokhov, L. Cincio, C. Coffrin
    arXiv, 2021, [arXiv]

  • Single-Qubit Fidelity Assessment of Quantum Annealing Hardware
    J. Nelson, M. Vuffray, A.Y. Lokhov, C. Coffrin
    IEEE Transactions on Quantum Engineering, 2021, [online], [arXiv]

  • Programmable Quantum Annealers as Noisy Gibbs Samplers
    M. Vuffray, C. Coffrin, Y.A. Kharkov, A.Y. Lokhov
    arXiv, 2020, [arXiv]

  • The Potential of Quantum Annealing for Rapid Solution Structure Identification
    Y. Pang, C. Coffrin, A.Y. Lokhov, M. Vuffray
    Constraints, 2020, [online], [arXiv]

  • Quantum Algorithm Implementations for Beginners
    J. Abhijith, …, S. Eidenbenz, P. J Coles, M. Vuffray, A.Y. Lokhov
    arXiv, 2018, [arXiv]

Energy Networks

  • The Impacts of Convex Piecewise Linear Cost Formulations on AC Optimal Power Flow
    C. Coffrin, B. Knueven, J. Holzer, M. Vuffray
    Electric Power Systems Research, 2021, [online], [arXiv]

  • Real-Time Anomaly Detection and Classification in Streaming PMU Data
    C. Hannon, D. Deka, D. Jin, M. Vuffray, A.Y. Lokhov
    IEEE Madrid PowerTech, 2021, [online], [arXiv]

  • Efficient Polynomial Chaos Expansion for Uncertainty Quantification in Power Systems
    D. Metivier, M. Vuffray, S. Misra
    Power Systems Computation Conference (PSCC), 2020, [online], [arXiv]

  • Uncovering Power Transmission Dynamic Model from Incomplete PMU Observations
    A.Y. Lokhov, D. Deka, M. Vuffray, M. Chertkov
    IEEE Conference on Decision and Control (CDC), 2018, [online]

  • Online Learning of Power Transmission Dynamics
    A.Y. Lokhov, M. Vuffray, D. Shemetov, D. Deka, M. Chertkov
    Power Systems Computation Conference (PSCC), 2018, [online], [arXiv]

  • Graphical Models for Optimal Power Flow
    K. Dvijotham, M. Chertkov, P. Van Hentenryck, M. Vuffray, S. Misra
    Constraints, 2017, [online], [arXiv]

  • Monotonicity of Dissipative Flow Networks Renders Robust Maximum Profit Problem Tractable: General Analysis and Application to Natural Gas Flows
    M. Vuffray, S. Misra, M. Chertkov
    IEEE Conference on Decision and Control (CDC), 2015, [online], [arXiv]

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