Open-source implementations of algorithms and models developed by myself and close collaborators, as well as datasets used in various research projects.
My page on GitHub: https://github.com/lokhov
- Julia implementation of RISE, logRISE and RPLE algorithms for the inverse Ising problem
https://github.com/lanl-ansi/inverse_ising
References:
M. Vuffray, S. Misra, A.Y. Lokhov, M. Chertkov, «Interaction screening: efficient and sample-optimal learning of Ising models», NIPS (2016)
A.Y. Lokhov, M. Vuffray, S. Misra, M. Chertkov, «Optimal structure and parameter learning of Ising models», Sci. Adv. (2018) - Julia implementation of Interaction Screening based algorithms for learning general graphical models
https://github.com/lanl-ansi/GraphicalModelLearning.jl
Reference:
M. Vuffray, S. Misra, A.Y. Lokhov, «Efficient learning of discrete graphical models», NeurIPS (2020) - Julia implementation of NeurISE algorithm for learning general graphical models
https://github.com/lanl-ansi/NeurISE
Reference:
Abhijith J., A.Y. Lokhov, S. Misra, M. Vuffray, «Learning of Discrete Graphical Models with Neural Networks», NeurIPS (2020) - Julia implementation of Dynamic Message-Passing algorithm for inference and learning for the Independent Cascade model
https://github.com/mateuszwilinski/dynamic-message-passing/
Reference:
M. Wilinski, A.Y. Lokhov, «Scalable Learning of Independent Cascade Dynamics from Partial Observations», arXiv (2020) - R implementation of anomaly detection from multivariate time series
https://github.com/lanl-ansi/MVAD
Reference:
C. Hannon et al., «Real-time Anomaly Detection and Classification in Streaming PMU Data», PowerTech (2021) - Julia implementation of the Quantum Annealing Single-qubit Assessment protocol
https://github.com/lanl-ansi/QASA
Reference:
J. Nelson, M. Vuffray, A. Y. Lokhov, C. Coffrin, «Single-Qubit Fidelity Assessment of Quantum Annealing Hardware», arXiv (2021)