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Anna Scaglione
Arizona State University
Distributed computingMathematical optimizationComputer networkMathematicsComputer science
417Publications
48H-index
11.6kCitations
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Publications 413
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#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 2
#2Anna Scaglione (ASU: Arizona State University)H-Index: 48
In this paper, we derive a Bayesian lower bound on the estimation of a scalar parameter whose prior distribution is assumed to have a bounded support. For such truncated prior distributions it is well known that the Bayesian Cramer-Rao bound (BCRB) does not hold. We also analyze the tightness of this bound for maximum a-posteriori estimators (MAP) in the case of conditionally Gaussian observations and highlight some interesting properties. Numerical results illustrate the tightness of this bound...
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#1Waheed U. Bajwa (RU: Rutgers University)H-Index: 21
#2Volkan Cevher (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 33
Last. Anna Scaglione (ASU: Arizona State University)H-Index: 48
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#1Eran Schweitzer (ASU: Arizona State University)H-Index: 3
#2Shammya Shananda Saha (ASU: Arizona State University)H-Index: 2
Last. Daniel Arnold (LBNL: Lawrence Berkeley National Laboratory)H-Index: 8
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A line loss approximation via parametrization is developed to improve performance of the simplified Baran and Wu DistFlow method, while maintaining a linear set of equations. The approach is evaluated on thousands of training feeders that are created to determine a numerically optimal setting for the parameterization. Feeders are generated using recent advances in synthetic network test case generation. The problem is formulated with the same structure as the simplified DistFlow, yet is more acc...
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Apr 11, 2020 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 2
#2Nurullah Karakoc (ASU: Arizona State University)H-Index: 2
Last. Anna Scaglione (ASU: Arizona State University)H-Index: 48
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#1Hoi To Wai (CUHK: The Chinese University of Hong Kong)
#1Hoi-To Wai (CUHK: The Chinese University of Hong Kong)H-Index: 11
Last. Anna Scaglione (ASU: Arizona State University)H-Index: 48
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This paper studies an acceleration technique for incremental aggregated gradient (IAG) method through the use of curvature information for solving strongly convex finite sum optimization problems. These optimization problems of interest arise in large-scale learning applications. Our technique utilizes a curvature-aided gradient tracking step to produce accurate gradient estimates incrementally using Hessian information. We propose and analyze two methods utilizing the new technique, the curvatu...
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#1Kari Hreinsson (ASU: Arizona State University)H-Index: 1
#2Anna Scaglione (ASU: Arizona State University)H-Index: 48
Last. Mahnoosh Alizadeh (UCSB: University of California, Santa Barbara)H-Index: 13
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#1Hoi-To WaiH-Index: 11
#2Santiago SegarraH-Index: 15
Last. Ali JadbabaieH-Index: 4
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This paper considers a new framework to detect communities in a graph from the observation of signals at its nodes. We model the observed signals as noisy outputs of an unknown network process, represented as a graph filter that is excited by a set of unknown low-rank inputs/excitations. Application scenarios of this model include diffusion dynamics, pricing experiments, and opinion dynamics. Rather than learning the precise parameters of the graph itself, we aim at retrieving the community stru...
3 CitationsSource
#1Mahdi JameiH-Index: 7
#2Raksha Ramakrishna (ASU: Arizona State University)H-Index: 2
Last. Sean Peisert (LBNL: Lawrence Berkeley National Laboratory)H-Index: 15
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Author(s): Jamei, Mahdi; Ramakrishna, Raksha; Tesfay, Teklemariam; Gentz, Reinhard; Roberts, Ciaran; Scaglione, Anna; Peisert, Sean
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#1Ciaran RobertsH-Index: 7
#2Anna ScaglioneH-Index: 48
Last. Daniel ArnoldH-Index: 8
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Author(s): Roberts, Ciaran; Scaglione, Anna; Jamei, Mahdi; Gentz, Reinhard; Peisert, Sean; Stewart, Emma M; McParland, Chuck; McEachern, Alex; Arnold, Daniel
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#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 2
#2Anna Scaglione (ASU: Arizona State University)H-Index: 48
Last. Andrey Bernstein (NREL: National Renewable Energy Laboratory)H-Index: 13
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In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explic...
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