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no code implementations • 10 Jun 2022 • Konstantinos D. Polyzos, Qin Lu, Georgios B. Giannakis

Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, and computer vision.

no code implementations • 27 May 2022 • Qin Lu, Konstantinos D. Polyzos, Bingcong Li, Georgios B. Giannakis

Tests on synthetic functions and real-world applications showcase the merits of the proposed method.

no code implementations • 22 Mar 2022 • Manish K. Singh, Sairaj Dhople, Florian Dorfler, Georgios B. Giannakis

Kron reduction is a network-reduction method that eliminates nodes with zero current injections from electrical networks operating in sinusoidal steady state.

no code implementations • 15 Feb 2022 • Mana Jalali, Manish K. Singh, Vassilis Kekatos, Georgios B. Giannakis, Chen-Ching Liu

Adjusting inverter injection setpoints for distributed energy resources can be an effective grid control mechanism.

no code implementations • 1 Dec 2021 • Qin Lu, Georgios B. Giannakis

Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous.

no code implementations • 20 Oct 2021 • Alireza Sadeghi, Meng Ma, Bingcong Li, Georgios B. Giannakis

The data distribution is considered unknown, but lies within a Wasserstein ball centered around empirical data distribution.

no code implementations • 13 Oct 2021 • Qin Lu, Georgios V. Karanikolas, Georgios B. Giannakis

Belonging to the family of Bayesian nonparametrics, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also in quantifying the associated uncertainty.

no code implementations • NeurIPS 2021 • Bingcong Li, Alireza Sadeghi, Georgios B. Giannakis

Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented merits in machine learning and signal processing applications.

no code implementations • 7 Oct 2021 • Panagiotis A. Traganitis, Georgios B. Giannakis

Despite its successes in various machine learning and data science tasks, crowdsourcing can be susceptible to attacks from dedicated adversaries.

no code implementations • 29 Sep 2021 • Bingcong Li, Shuai Zheng, Parameswaran Raman, Anshumali Shrivastava, Georgios B. Giannakis

On-device memory concerns in distributed deep learning are becoming more severe due to i) the growth of model size in multi-GPU training, and ii) the adoption of neural networks for federated learning on IoT devices with limited storage.

no code implementations • 25 Jun 2021 • Mingliang Xiong, Qingwen Liu, Gang Wang, Georgios B. Giannakis, Sihai Zhang, Jinkang Zhu, Chuan Huang

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier.

no code implementations • 10 Jun 2021 • Wen Fang, Hao Deng, Qingwen Liu, Mingqing Liu, Mengyuan Xu, Liuqing Yang, Georgios B. Giannakis

Integrating the wireless power transfer (WPT) technology into the wireless communication system has been important for operational cost saving and power-hungry problem solving of electronic devices.

no code implementations • 17 Apr 2021 • Konstantinos D. Polyzos, Costas Mavromatis, Vassilis N. Ioannidis, Georgios B. Giannakis

Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and network science applications including {the Internet of Things (IoT)}, finance, security, to list a few.

no code implementations • 27 Mar 2021 • Manish K. Singh, Vassilis Kekatos, Georgios B. Giannakis

To shift the computational burden from real-time to offline in delay-critical power systems applications, recent works entertain the idea of using a deep neural network (DNN) to predict the solutions of the AC optimal power flow (AC-OPF) once presented load demands.

no code implementations • 20 Dec 2020 • Panagiotis A. Traganitis, Georgios B. Giannakis

Crowdsourcing has emerged as a powerful paradigm for efficiently labeling large datasets and performing various learning tasks, by leveraging crowds of human annotators.

no code implementations • 10 Dec 2020 • Lingda Wang, Bingcong Li, Huozhi Zhou, Georgios B. Giannakis, Lav R. Varshney, Zhizhen Zhao

The second algorithm, \texttt{EXP3-LGC-IX}, is developed for a special class of problems, for which the regret is reduced to $\mathcal{O}(\sqrt{\alpha(G)dT\log{K}\log(KT)})$ for both directed as well as undirected feedback graphs.

no code implementations • 9 Dec 2020 • Bingcong Li, Lingda Wang, Georgios B. Giannakis, Zhizhen Zhao

Relying on no problem dependent parameters in the step sizes, the convergence rate of ExtraFW for general convex problems is shown to be ${\cal O}(\frac{1}{k})$, which is optimal in the sense of matching the lower bound on the number of solved FW subproblems.

no code implementations • 7 Jul 2020 • Alireza Sadeghi, Gang Wang, Meng Ma, Georgios B. Giannakis

This robust learning task entails an infinite-dimensional optimization problem, which is challenging.

no code implementations • 19 Jun 2020 • Bingcong Li, Mario Coutino, Georgios B. Giannakis, Geert Leus

We unveil the connections between Frank Wolfe (FW) type algorithms and the momentum in Accelerated Gradient Methods (AGM).

no code implementations • 17 Jun 2020 • Yanjie Dong, Georgios B. Giannakis, Tianyi Chen, Julian Cheng, Md. Jahangir Hossain, Victor C. M. Leung

For strongly convex loss functions, FRPG and LFRPG have provably faster convergence rates than a benchmark robust stochastic aggregation algorithm.

no code implementations • 19 May 2020 • Alireza Sadeghi, Georgios B. Giannakis, Gang Wang, Fatemeh Sheikholeslami

With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks.

no code implementations • ICLR 2020 • Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis

Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks.

no code implementations • 18 Apr 2020 • Mingliang Xiong, Qingwen Liu, Gang Wang, Georgios B. Giannakis, Chuan Huang

Wireless optical communications (WOC) has carriers up to several hundred terahertz, which offers several advantages, such as ultrawide bandwidth and no electromagnetic interference.

no code implementations • 15 Mar 2020 • Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis

The era of "data deluge" has sparked renewed interest in graph-based learning methods and their widespread applications ranging from sociology and biology to transportation and communications.

no code implementations • 3 Mar 2020 • Qiuling Yang, Alireza Sadeghi, Gang Wang, Georgios B. Giannakis, Jian Sun

Numerical tests using real load data on the IEEE $118$-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives.

no code implementations • 27 Jan 2020 • Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis

The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs.

no code implementations • 29 Dec 2019 • Zhaoxian Wu, Qing Ling, Tianyi Chen, Georgios B. Giannakis

This motivates us to reduce the variance of stochastic gradients as a means of robustifying SGD in the presence of Byzantine attacks.

no code implementations • 3 Nov 2019 • Jun Sun, Gang Wang, Georgios B. Giannakis, Qinmin Yang, Zaiyue Yang

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice.

no code implementations • 25 Oct 2019 • Qiuling Yang, Alireza Sadeghi, Gang Wang, Georgios B. Giannakis, Jian Sun

Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs.

no code implementations • 21 Oct 2019 • Vassilis N. Ioannidis, Georgios B. Giannakis

Graph convolutional networks (GCNs) are vulnerable to perturbations of the graph structure that are either random, or, adversarially designed.

no code implementations • 21 Oct 2019 • Vassilis N. Ioannidis, Dimitris Berberidis, Georgios B. Giannakis

Alleviating this limitation, GraphSAC randomly draws subsets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employing a semi-supervised learning (SSL) module to estimate nominal label distributions per node.

no code implementations • 15 Oct 2019 • Bingcong Li, Georgios B. Giannakis

The main goal of this work is equipping convex and nonconvex problems with Barzilai-Borwein (BB) step size.

1 code implementation • NeurIPS 2019 • Jun Sun, Tianyi Chen, Georgios B. Giannakis, Zaiyue Yang

The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication.

no code implementations • 10 Sep 2019 • Gang Wang, Bingcong Li, Georgios B. Giannakis

Motivated by the widespread use of temporal-difference (TD-) and Q-learning algorithms in reinforcement learning, this paper studies a class of biased stochastic approximation (SA) procedures under a mild "ergodic-like" assumption on the underlying stochastic noise sequence.

no code implementations • 5 Sep 2019 • Donghoon Lee, Georgios B. Giannakis

Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks.

no code implementations • ICML 2020 • Bingcong Li, Lingda Wang, Georgios B. Giannakis

Then a simple yet effective means to adjust the number of iterations per inner loop is developed to enhance the merits of the proposed averaging schemes and BB step sizes.

no code implementations • 22 Jun 2019 • Panagiotis A. Traganitis, Georgios B. Giannakis

data, the present work introduces an unsupervised scheme for learning from ensembles of classifiers in the presence of data dependencies.

no code implementations • 20 Jun 2019 • Pere Giménez-Febrer, Alba Pagès-Zamora, Georgios B. Giannakis

This paper analyzes the generalization error of such approaches, and presents numerical tests confirming the theoretical results.

no code implementations • 5 Jun 2019 • Bingcong Li, Meng Ma, Georgios B. Giannakis

For convex problems, when adopting an $n$-dependent step size, the complexity of L2S is ${\cal O}(n+ \sqrt{n}/\epsilon)$; while for more frequently adopted $n$-independent step size, the complexity is ${\cal O}(n+ n/\epsilon)$.

no code implementations • 5 Apr 2019 • Fatemeh Sheikholeslami, Swayambhoo Jain, Georgios B. Giannakis

The effectiveness of the novel detectors in the context of competing alternatives is highlighted through extensive tests for various types of adversarial attacks with variable levels of strength.

no code implementations • 27 Feb 2019 • Alireza Sadeghi, Gang Wang, Georgios B. Giannakis

To handle the large continuous state space, a scalable deep reinforcement learning approach is pursued.

no code implementations • 17 Dec 2018 • Alireza Sadeghi, Fatemeh Sheikholeslami, Antonio G. Marques, Georgios B. Giannakis

Under this generic formulation, first by considering stationary distributions for the costs and file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem.

no code implementations • 7 Dec 2018 • Tianyi Chen, Kaiqing Zhang, Georgios B. Giannakis, Tamer Başar

This paper deals with distributed policy optimization in reinforcement learning, which involves a central controller and a group of learners.

no code implementations • 3 Dec 2018 • Yanning Shen, Geert Leus, Georgios B. Giannakis

Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes.

no code implementations • 29 Nov 2018 • Jia Chen, Gang Wang, Georgios B. Giannakis

common sources).

1 code implementation • 27 Nov 2018 • Dimitris Berberidis, Georgios B. Giannakis

Moreover, an algorithmic scheme is proposed for training the model parameters effieciently and in an unsupervised manner.

3 code implementations • 15 Nov 2018 • Liang Zhang, Gang Wang, Georgios B. Giannakis

To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring.

1 code implementation • 9 Nov 2018 • Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, Qing Ling

In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers.

1 code implementation • 5 Nov 2018 • Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis

The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering.

1 code implementation • 22 Sep 2018 • Vassilis N. Ioannidis, Ahmed S. Zamzam, Georgios B. Giannakis, Nicholas D. Sidiropoulos

The resulting community detection approach is successful even when some links in the graphs are missing.

no code implementations • 14 Aug 2018 • Gang Wang, Georgios B. Giannakis, Jie Chen

In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted.

no code implementations • 1 Aug 2018 • Pere Giménez-Febrer, Alba Pagès-Zamora, Georgios B. Giannakis

Matrix completion and extrapolation (MCEX) are dealt with here over reproducing kernel Hilbert spaces (RKHSs) in order to account for prior information present in the available data.

no code implementations • 9 Jul 2018 • Bingcong Li, Tianyi Chen, Georgios B. Giannakis

This paper deals with bandit online learning problems involving feedback of unknown delay that can emerge in multi-armed bandit (MAB) and bandit convex optimization (BCO) settings.

1 code implementation • NeurIPS 2018 • Tianyi Chen, Georgios B. Giannakis, Tao Sun, Wotao Yin

This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation.

no code implementations • 16 May 2018 • Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis

Alleviating the limited flexibility of existing approaches, this work advocates structural models for graph processes and develops novel algorithms for joint inference of the network topology and processes from partial nodal observations.

no code implementations • 15 May 2018 • Jia Chen, Gang Wang, Georgios B. Giannakis

Under certain conditions, dPCA is proved to be least-squares optimal in recovering the component vector unique to the target data relative to background data.

no code implementations • 9 May 2018 • Bingcong Li, Tianyi Chen, Georgios B. Giannakis

To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security.

no code implementations • 6 Apr 2018 • Donghoon Lee, Dimitris Berberidis, Georgios B. Giannakis

Key to success of RTI is to model accurately the shadowing effects as the bi-dimensional integral of the SLF scaled by a weight function, which is estimated using regularized regression.

Signal Processing Applications

no code implementations • 5 Apr 2018 • Dimitris Berberidis, Athanasios N. Nikolakopoulos, Georgios B. Giannakis

Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements.

no code implementations • 27 Mar 2018 • Jia Chen, Gang Wang, Yanning Shen, Georgios B. Giannakis

Canonical correlation analysis (CCA) is a powerful technique for discovering whether or not hidden sources are commonly present in two (or more) datasets.

no code implementations • 29 Jan 2018 • Yanning Shen, Panagiotis A. Traganitis, Georgios B. Giannakis

The novel framework encompasses most of the existing dimensionality reduction methods, but it is also capable of capturing and preserving possibly nonlinear correlations that are ignored by linear methods.

no code implementations • 28 Dec 2017 • Yanning Shen, Tianyi Chen, Georgios B. Giannakis

To further boost performance in dynamic environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed.

no code implementations • 8 Dec 2017 • Panagiotis A. Traganitis, Alba Pagès-Zamora, Georgios B. Giannakis

The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools.

no code implementations • 28 Nov 2017 • Vassilis N. Ioannidis, Meng Ma, Athanasios N. Nikolakopoulos, Georgios B. Giannakis, Daniel Romero

The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced.

no code implementations • 25 Nov 2017 • Vassilis N. Ioannidis, Daniel Romero, Georgios B. Giannakis

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications.

no code implementations • 25 Oct 2017 • Gang Wang, Jia Chen, Georgios B. Giannakis

Principal component analysis (PCA) has well-documented merits for data extraction and dimensionality reduction.

no code implementations • 27 Jul 2017 • Tianyi Chen, Georgios B. Giannakis

Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddle-point (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment.

no code implementations • 22 Jul 2017 • Panagiotis A. Traganitis, Georgios B. Giannakis

The immense amount of daily generated and communicated data presents unique challenges in their processing.

no code implementations • 19 Jul 2017 • Alireza Sadeghi, Fatemeh Sheikholeslami, Georgios B. Giannakis

To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests.

Networking and Internet Architecture

no code implementations • 29 May 2017 • Gang Wang, Georgios B. Giannakis, Yousef Saad, Jie Chen

This paper deals with finding an $n$-dimensional solution $x$ to a system of quadratic equations of the form $y_i=|\langle{a}_i, x\rangle|^2$ for $1\le i \le m$, which is also known as phase retrieval and is NP-hard in general.

no code implementations • 19 May 2017 • Dimitris Berberidis, Georgios B. Giannakis

Leveraging the graph for classification builds on the premise that labels across neighboring nodes are correlated according to a categorical Markov random field (MRF).

no code implementations • 14 Jan 2017 • Tianyi Chen, Qing Ling, Georgios B. Giannakis

Performance of an online algorithm in this setting is assessed by: i) the difference of its losses relative to the best dynamic solution with one-slot-ahead information of the loss function and the constraint (that is here termed dynamic regret); and, ii) the accumulated amount of constraint violations (that is here termed dynamic fit).

no code implementations • 27 Dec 2016 • Liang Zhang, Gang Wang, Daniel Romero, Georgios B. Giannakis

To circumvent the limitations of existing methods, the present work develops step sizes for RB-FW that enable a flexible selection of the number of blocks to update per iteration while ensuring convergence and feasibility of the iterates.

no code implementations • 12 Dec 2016 • Daniel Romero, Vassilis N. Ioannidis, Georgios B. Giannakis

Graph-based methods pervade the inference toolkits of numerous disciplines including sociology, biology, neuroscience, physics, chemistry, and engineering.

1 code implementation • 23 Nov 2016 • Gang Wang, Liang Zhang, Georgios B. Giannakis, Mehmet Akcakaya, Jie Chen

Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently, a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and, in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations.

Information Theory Information Theory Optimization and Control

no code implementations • 29 Oct 2016 • Gang Wang, Georgios B. Giannakis, Jie Chen

A novel approach termed \emph{stochastic truncated amplitude flow} (STAF) is developed to reconstruct an unknown $n$-dimensional real-/complex-valued signal $\bm{x}$ from $m$ `phaseless' quadratic equations of the form $\psi_i=|\langle\bm{a}_i,\bm{x}\rangle|$.

no code implementations • 26 Oct 2016 • Yanning Shen, Brian Baingana, Georgios B. Giannakis

The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the well-known parallel factor (PARAFAC) decomposition.

no code implementations • 20 Oct 2016 • Yanning Shen, Brian Baingana, Georgios B. Giannakis

To unify these complementary perspectives, linear structural vector autoregressive models (SVARMs) that leverage both contemporaneous and time-lagged nodal data have recently been put forth.

no code implementations • 7 Oct 2016 • Tianyi Chen, Aryan Mokhtari, Xin Wang, Alejandro Ribeiro, Georgios B. Giannakis

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements.

no code implementations • 27 Sep 2016 • Yanning Shen, Morteza Mardani, Georgios B. Giannakis

The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios.

no code implementations • 14 Sep 2016 • Morteza Mardani, Georgios B. Giannakis, Kamil Ugurbil

Alteranating majorization minimization is adopted to develop online algorithms that recursively procure the reconstruction upon arrival of a new undersampled $k$-space frame.

no code implementations • 28 Jun 2016 • Brian Baingana, Georgios B. Giannakis

Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies.

no code implementations • 7 Jun 2016 • Daniel Romero, Seung-Jun Kim, Georgios B. Giannakis, Roberto Lopez-Valcarce

Power spectral density (PSD) maps providing the distribution of RF power across space and frequency are constructed using power measurements collected by a network of low-cost sensors.

no code implementations • 26 May 2016 • Gang Wang, Georgios B. Giannakis, Yonina C. Eldar

This paper presents a new algorithm, termed \emph{truncated amplitude flow} (TAF), to recover an unknown vector $\bm{x}$ from a system of quadratic equations of the form $y_i=|\langle\bm{a}_i,\bm{x}\rangle|^2$, where $\bm{a}_i$'s are given random measurement vectors.

no code implementations • 23 May 2016 • Daniel Romero, Meng Ma, Georgios B. Giannakis

A number of SPoG notions such as bandlimitedness, graph filters, and the graph Fourier transform are naturally accommodated in the kernel framework.

no code implementations • 10 May 2016 • Yanning Shen, Brian Baingana, Georgios B. Giannakis

Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, and is amenable to proximal-splitting optimization methods.

no code implementations • 28 Jan 2016 • Fatemeh Sheikholeslami, Dimitris Berberidis, Georgios B. Giannakis

Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks.

no code implementations • 6 Oct 2015 • Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis

At the heart of SkeVa-SC lies a randomized scheme for approximating the underlying probability density function of the observed data by kernel smoothing arguments.

no code implementations • 27 Jul 2015 • Dimitris Berberidis, Vassilis Kekatos, Georgios B. Giannakis

Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability.

no code implementations • 25 Jun 2015 • Brian Baingana, Georgios B. Giannakis

Efficient tracking algorithms suitable for both online and decentralized operation are developed.

no code implementations • 30 Mar 2015 • Georgios B. Giannakis, Qing Ling, Gonzalo Mateos, Ioannis D. Schizas, Hao Zhu

This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data.

no code implementations • 29 Jan 2015 • Konstantinos Slavakis, Georgios B. Giannakis

Applications involving dictionary learning, non-negative matrix factorization, subspace clustering, and parallel factor tensor decomposition tasks motivate well algorithms for per-block-convex and non-smooth optimization problems.

no code implementations • 22 Jan 2015 • Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis

In response to the need for learning tools tuned to big data analytics, the present paper introduces a framework for efficient clustering of huge sets of (possibly high-dimensional) data.

no code implementations • 22 Oct 2014 • Vassilis Kekatos, Georgios B. Giannakis, Ross Baldick

Grid security and open markets are two major smart grid goals.

no code implementations • 30 Sep 2014 • Swayambhoo Jain, Seung-Jun Kim, Georgios B. Giannakis

Dynamic clustered cooperation, where the sparse equalizer and the cooperation clusters are jointly determined, is solved via alternating minimization based on spectral clustering and group-sparse regression.

no code implementations • 17 Apr 2014 • Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis

In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from \emph{incomplete streaming} data.

no code implementations • 17 Jan 2014 • Brian Baingana, Georgios B. Giannakis

Visual rendering of graphs is a key task in the mapping of complex network data.

no code implementations • 2 Dec 2013 • Vassilis Kekatos, Georgios B. Giannakis, Ross Baldick

The potential of recovering the topology of a grid using solely publicly available market data is explored here.

no code implementations • 2 Oct 2013 • Vassilis Kekatos, Yu Zhang, Georgios B. Giannakis

The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure.

1 code implementation • 4 Apr 2012 • Vassilis Kekatos, Georgios B. Giannakis

Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE).

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