no code implementations • 20 Dec 2023 • Mateusz Wilinski, Andrey Y. Lokhov
Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data.
no code implementations • 30 Sep 2023 • Melvyn Tyloo, Marc Vuffray, Andrey Y. Lokhov
Malfunctioning equipment, erroneous operating conditions or periodic load variations can cause periodic disturbances that would persist over time, creating an undesirable transfer of energy across the system -- an effect referred to as forced oscillations.
1 code implementation • 8 Apr 2023 • Abhijith Jayakumar, Marc Vuffray, Andrey Y. Lokhov
An ideal representation of a quantum state combines a succinct characterization informed by the system's structure and symmetries, along with the ability to predict the physical observables of interest.
1 code implementation • 4 Oct 2022 • Mateusz Wilinski, Lauren Castro, Jeffrey Keithley, Carrie Manore, Josefina Campos, Ethan Romero-Severson, Daryl Domman, Andrey Y. Lokhov
Cholera continues to be a global health threat.
no code implementations • 3 Sep 2021 • Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Tameem Albash, Carleton Coffrin
This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs sampling performance.
Combinatorial Optimization Vocal Bursts Intensity Prediction
1 code implementation • 7 Apr 2021 • Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Carleton Coffrin
Overall, the proposed QASA protocol provides a useful tool for assessing the performance of current and emerging quantum annealing devices.
1 code implementation • 2 Apr 2021 • Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra
We observe that for samples coming from a dynamical process far from equilibrium, the sample complexity reduces exponentially compared to a dynamical process that mixes quickly.
1 code implementation • 18 Feb 2021 • Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
We address the problem of learning of continuous exponential family distributions with unbounded support.
no code implementations • 16 Dec 2020 • Marc Vuffray, Carleton Coffrin, Yaroslav A. Kharkov, Andrey Y. Lokhov
Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including powerful machine learning frameworks such as deep learning.
1 code implementation • 13 Jul 2020 • Mateusz Wilinski, Andrey Y. Lokhov
Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting.
1 code implementation • NeurIPS 2020 • Abhijith J., Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray
In addition, we also show a variant of NeurISE that can be used to learn a neural net representation for the full energy function of the true model.
no code implementations • 29 Dec 2019 • Andrey Y. Lokhov, David Saad
The resulting saving of a potentially large sampling factor in the running time compared to simulation-based techniques hence makes it possible to address large-scale problem instances.
no code implementations • 14 Nov 2019 • Christopher Hannon, Deepjyoti Deka, Dong Jin, Marc Vuffray, Andrey Y. Lokhov
Ensuring secure and reliable operations of the power grid is a primary concern of system operators.
1 code implementation • NeurIPS 2020 • Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov
We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models.
no code implementations • 27 Oct 2017 • Andrey Y. Lokhov, Marc Vuffray, Dmitry Shemetov, Deepjyoti Deka, Michael Chertkov
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations.
no code implementations • 15 Mar 2017 • Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
What is the optimal number of independent observations from which a sparse Gaussian Graphical Model can be correctly recovered?
1 code implementation • 15 Dec 2016 • Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov
Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning.
no code implementations • NeurIPS 2016 • Andrey Y. Lokhov
Spreading processes are often modelled as a stochastic dynamics occurring on top of a given network with edge weights corresponding to the transmission probabilities.
no code implementations • NeurIPS 2016 • Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov
We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.
no code implementations • 23 Sep 2015 • Andrey Y. Lokhov, Theodor Misiakiewicz
A number of recent papers introduced efficient algorithms for the estimation of spreading parameters, based on the maximization of the likelihood of observed cascades, assuming that the full information for all the nodes in the network is available.