no code implementations • 4 Mar 2024 • Nikhil Ravi, Anna Scaglione, Sean Peisert, Parth Pradhan
In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data.
no code implementations • 9 Nov 2023 • Andrew Campbell, Hang Liu, Leah Woldemariam, Anna Scaglione
Recent research indicates that frequent model communication stands as a major bottleneck to the efficiency of decentralized machine learning (ML), particularly for large-scale and over-parameterized neural networks (NNs).
no code implementations • 27 Jun 2023 • Hang Liu, Anna Scaglione, Hoi-To Wai
Our analysis shows that the blind matching outcome converges to the result obtained with known graph topologies when the signal sampling size is large and the signal noise is small.
no code implementations • 8 Jun 2023 • Raksha Ramakrishna, Anna Scaglione, Tong Wu, Nikhil Ravi, Sean Peisert
In this paper, we present a notion of differential privacy (DP) for data that comes from different classes.
no code implementations • 7 Apr 2023 • Nikhil Ravi, Anna Scaglione, Julieta Giraldez, Parth Pradhan, Chuck Moran, Sean Peisert
Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations.
no code implementations • 21 Feb 2023 • Tong Wu, Anna Scaglione, Daniel Arnold
This paper presents a novel primal-dual approach for learning optimal constrained DRL policies for dynamic optimal power flow problems, with the aim of controlling power generations and battery outputs.
no code implementations • 17 Aug 2022 • Tong Wu, Anna Scaglione, Daniel Arnold
The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention.
no code implementations • 31 Mar 2022 • Tong Wu, Ignacio Losada Carreno, Anna Scaglione, Daniel Arnold
This paper proposes a model-free Volt-VAR control (VVC) algorithm via the spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL) framework, whose goal is to control smart inverters in an unbalanced distribution system.
no code implementations • 27 Jan 2022 • Daniel Arnold, Sy-Toan Ngo, Ciaran Roberts, Yize Chen, Anna Scaglione, Sean Peisert
Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems.
no code implementations • 7 Dec 2021 • Nikhil Ravi, Anna Scaglione, Sachin Kadam, Reinhard Gentz, Sean Peisert, Brent Lunghino, Emmanuel Levijarvi, Aram Shumavon
It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers.
no code implementations • 10 Mar 2021 • Raksha Ramakrishna, Anna Scaglione
The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework.
no code implementations • 4 Aug 2020 • Raksha Ramakrishna, Hoi-To Wai, Anna Scaglione
The notion of graph filters can be used to define generative models for graph data.
no code implementations • 5 Sep 2018 • Hoi-To Wai, Santiago Segarra, Asuman E. Ozdaglar, Anna Scaglione, Ali Jadbabaie
The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals.
1 code implementation • 31 May 2018 • Hoi-To Wai, Wei Shi, Cesar A. Uribe, Angelia Nedich, Anna Scaglione
This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems.
no code implementations • 22 Mar 2018 • Hoi-To Wai, Nikolaos M. Freris, Angelia Nedic, Anna Scaglione
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems.
no code implementations • 24 Oct 2017 • Hoi-To Wai, Wei Shi, Angelia Nedic, Anna Scaglione
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method.
no code implementations • 20 Dec 2016 • Hoi-To Wai, Anna Scaglione, Uzi Harush, Baruch Barzel, Amir Leshem
To overcome this challenge, we develop the Robust IDentification of Sparse networks (RIDS) method that reconstructs the GRN from a small number of perturbation experiments.
no code implementations • 5 Dec 2016 • Hoi-To Wai, Jean Lafond, Anna Scaglione, Eric Moulines
The convergence of the proposed algorithm is studied by viewing the decentralized algorithm as an inexact FW algorithm.
no code implementations • 14 Sep 2016 • Marko Angjelichinoski, Anna Scaglione, Petar Popovski, Cedomir Stefanovic
We propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters.
no code implementations • 24 Jun 2016 • Lin Li, Ananthram Swami, Anna Scaglione
We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources.
no code implementations • 21 Jan 2016 • Hoi-To Wai, Anna Scaglione, Amir Leshem
The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i. e., agents whose opinions are not influenced by their neighbors.