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Georgios B. Giannakis
University of Minnesota
1,272Publications
112H-index
51.3kCitations
Publications 1272
Newest
Published on Feb 1, 2019in IEEE Internet of Things Journal 5.87
Tianyi Chen8
Estimated H-index: 8
(University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota)
This paper deals with online convex optimization involving both time-varying loss functions, and time-varying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (also known as bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing ...
13 Citations Source Cite
Published on Jan 1, 2019in IEEE Transactions on Smart Grid 7.37
Gang Wang8
Estimated H-index: 8
(University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota),
JieCHEN27
Estimated H-index: 27
(Beijing Institute of Technology)
In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow, hence inadequate for rea...
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Published on Jan 1, 2019in Journal of Machine Learning Research 2.28
Yanning Shen8
Estimated H-index: 8
,
Tianyi Chen8
Estimated H-index: 8
,
Georgios B. Giannakis112
Estimated H-index: 112
Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops a ...
Published on Mar 1, 2019in IEEE Transactions on Smart Grid 7.37
Bingcong Li1
Estimated H-index: 1
(Fudan University),
Tianyi Chen8
Estimated H-index: 8
(University of Minnesota)
+ 1 AuthorsGeorgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota)
Energy storage units hold promise to transform the electric power industry, since they can supply power to end customers during peak demand times, and operate as customers upon a power surplus. This paper studies online energy management with renewable energy resources and energy storage units. For the problem at hand, the popular approaches rely on stochastic dual (sub)gradient (SDG) iterations for a chosen stepsize ${\mu }$ , which generally require battery capacity ${\mathcal{ O}}{(1/\mu)}$ t...
2 Citations Source Cite
Published on Apr 15, 2019in IEEE Transactions on Signal Processing 4.20
Donghoon Lee2
Estimated H-index: 2
(University of Minnesota),
Dimitris Berberidis3
Estimated H-index: 3
(University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota)
Radio tomographic imaging (RTI) is an emerging technology to locate physical objects in a geographical area covered by wireless networks. From the attenuation measurements collected at spatially distributed sensors, radio tomography capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at each location along the propagation path. These SLFs can be utilized for interference management in wireless communication networks, environmental monitoring, and survivor ...
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Published on Mar 1, 2019in IEEE Transactions on Signal Processing 4.20
Dimitris Berberidis3
Estimated H-index: 3
(University of Minnesota),
Athanasios N. Nikolakopoulos4
Estimated H-index: 4
(University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota)
Diffusion-based classifiers such as those relying on the Personalized PageRank and the heat kernel enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen diffusion captures a typically unknown label propagation mechanism, which can be specific to the underlying graph, and potentially different for each class. This paper introduces a disciplined, data-efficient approach to learning class-specific diffu...
3 Citations Source Cite
Published on May 1, 2019in IEEE Transactions on Signal Processing 4.20
Gang Wang8
Estimated H-index: 8
(University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota),
JieCHEN27
Estimated H-index: 27
(Beijing Institute of Technology)
Neural networks with rectified linear unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being, e.g., Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data ar...
7 Citations Source Cite
Published on May 1, 2019in IEEE Transactions on Signal Processing 4.20
Vassilis N. Ioannidis2
Estimated H-index: 2
(University of Minnesota),
Yanning Shen8
Estimated H-index: 8
(University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota)
A task of major practical importance in network science is inferring the graph structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all nodes. However, application-specific constraints may prevent acquiring network-wide observations. Alleviating the limited flexibility of existing approaches, this work advocates structural models for graph processes and develops novel algorithms for join...
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Published on Feb 1, 2019in IEEE Transactions on Signal Processing 4.20
Jia Chen3
Estimated H-index: 3
(University of Minnesota),
Gang Wang8
Estimated H-index: 8
(University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(University of Minnesota)
Principal component analysis (PCA) is widely used for feature extraction and dimensionality reduction, with documented merits in diverse tasks involving high-dimensional data. PCA copes with one dataset at a time, but it is challenged when it comes to analyzing multiple datasets jointly. In certain data science settings however, one is often interested in extracting the most discriminative information from one dataset of particular interest (a.k.a. target data) relative to the other(s) (a.k.a. b...
1 Citations Source Cite
Published on Apr 1, 2019in arXiv: Systems and Control
Tianyi Chen8
Estimated H-index: 8
(University of Minnesota),
Sergio Barbarossa44
Estimated H-index: 44
(Sapienza University of Rome)
+ 2 AuthorsZhi-Li Zhang1
Estimated H-index: 1
(University of Minnesota)
Internet of Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to a massive number o...
2 Citations Source Cite
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