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04/08/2023
Learning Energy-Based Representations of Quantum Many-Body States
Efficient representation of quantum many-body states on classical comput...
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09/03/2021
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
Quantum Annealing (QA) was originally intended for accelerating the solu...
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04/07/2021
Single-Qubit Fidelity Assessment of Quantum Annealing Hardware
As a wide variety of quantum computing platforms become available, metho...
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04/02/2021
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
The usual setting for learning the structure and parameters of a graphic...
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02/18/2021
Learning Continuous Exponential Families Beyond Gaussian
We address the problem of learning of continuous exponential family dist...
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12/16/2020
Programmable Quantum Annealers as Noisy Gibbs Samplers
Drawing independent samples from high-dimensional probability distributi...
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07/13/2020
Scalable Learning of Independent Cascade Dynamics from Partial Observations
Spreading processes play an increasingly important role in modeling for ...
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12/29/2019
Scalable Influence Estimation Without Sampling
In a diffusion process on a network, how many nodes are expected to be i...
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11/14/2019
Real-time Anomaly Detection and Classification in Streaming PMU Data
Ensuring secure and reliable operations of the power grid is a primary c...
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02/02/2019
Efficient Learning of Discrete Graphical Models
Graphical models are useful tools for describing structured high-dimensi...
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10/27/2017
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of tr...
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08/31/2016
Reconstructing parameters of spreading models from partial observations
Spreading processes are often modelled as a stochastic dynamics occurrin...
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05/24/2016
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
We consider the problem of learning the underlying graph of an unknown I...
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09/23/2015