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Georgios B. Giannakis
University of Minnesota
1,238Publications
113H-index
49.1kCitations
Publications 1238
Newest
Jia Chen1
Estimated H-index: 1
(University of Minnesota),
Gang Wang8
Estimated H-index: 8
(University of Minnesota),
Georgios B. Giannakis113
Estimated H-index: 113
(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...
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2019 in Sport PsychologistIF: 1.35
Dimitris Berberidis3
Estimated H-index: 3
,
Athanasios N. Nikolakopoulos4
Estimated H-index: 4
,
Georgios B. Giannakis113
Estimated H-index: 113
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, that can be specific to the underlying graph, and potentially different for each class. The present work introduces a disciplined, data-efficient approach to learning class-specific...
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2019 in National Conference on Artificial Intelligence
Liping Li (University of Science and Technology of China), Wei Xu (University of Science and Technology of China)+ 2 AuthorsQing Ling18
Estimated H-index: 18
(Sun Yat-sen University)
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. The Byzantine workers, during the learning process, may send arbitrary incorrect messages to the master due to data corruptions, communication failures or malicious attacks, and consequently bias the learned model. The key to the proposed methods is a regularization term incorporated with the objective function so a...
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2018
Dimitris Berberidis3
Estimated H-index: 3
,
Athanasios N. Nikolakopoulos4
Estimated H-index: 4
,
Georgios B. Giannakis113
Estimated H-index: 113
Source
Bingcong Li , Tianyi Chen6
Estimated H-index: 6
,
Georgios B. Giannakis113
Estimated H-index: 113
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. MAB and BCO require only values of the objective function involved that become available through feedback, and are used to estimate the gradient appearing in the corresponding iterative algorithms. Since the challenging case of feedback with \emph{unknown} delays prevents one from constructing the sought gradient esti...
Liang Zhang4
Estimated H-index: 4
,
Gang Wang8
Estimated H-index: 8
,
Georgios B. Giannakis113
Estimated H-index: 113
Contemporary power grids are being challenged by rapid voltage fluctuations that are caused by large-scale deployment of renewable generation, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large scale and nonconvexity however, the existing power system state estimation (PSSE) schemes become computationally expensive or yield suboptimal performance. To bypass these hurdles,...
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2018
Jia Chen1
Estimated H-index: 1
,
Gang Wang8
Estimated H-index: 8
,
Georgios B. Giannakis113
Estimated H-index: 113
Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional representations encountered with multiview data of shared entities (a.k.a. common sources). However, existing MCCA approaches do not exploit the geometry of the common sources, which may be available \emph{a priori}, or can be constructed using certain domain knowledge. This prior information about the common sources can be encoded by a graph, and be invoked as a regularizer to enrich the maximum variance MCCA framework...
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Dimitris Berberidis3
Estimated H-index: 3
,
Georgios B. Giannakis113
Estimated H-index: 113
Node embedding is the task of extracting informative and descriptive features over the nodes of a graph. The importance of node embeddings for graph analytics, as well as learning tasks such as node classification, link prediction and community detection, has led to increased interest on the problem leading to a number of recent advances. Much like PCA in the feature domain, node embedding is an inherently \emph{unsupervised} task; in lack of metadata used for validation, practical methods may r...
Source
Meng Ma2
Estimated H-index: 2
(University of Minnesota),
Athanasios N. Nikolakopoulos4
Estimated H-index: 4
(University of Minnesota),
Georgios B. Giannakis113
Estimated H-index: 113
(University of Minnesota)
The present work introduces the hybrid consensus alternating direction method of multipliers (H-CADMM), a novel framework for optimization over networks which unifies existing distributed optimization approaches, including the centralized and the decentralized consensus ADMM. H-CADMM provides a flexible tool that leverages the underlying graph topology in order to achieve a desirable sweet spot between node-to-node communication overhead and rate of convergence—thereby alleviating known limitati...
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