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Georgios B. Giannakis
University of Minnesota
1,235Publications
113H-index
49kCitations
Publications 1235
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Liping Li (University of Science and Technology of China), Wei Xu (University of Science and Technology of China), Tianyi Chen6
Estimated H-index: 6
(University of Minnesota)
... more
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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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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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...
Georgios B. Giannakis113
Estimated H-index: 113
(University of Minnesota),
Yanning Shen4
Estimated H-index: 4
(University of Minnesota),
Georgios Vasileios Karanikolas1
Estimated H-index: 1
(University of Minnesota)
Identifying graph topologies as well as processes evolving over graphs emerge in various applications involving gene-regulatory, brain, power, and social networks, to name a few. Key graph-aware learning tasks include regression, classification, subspace clustering, anomaly identification, interpolation, extrapolation, and dimensionality reduction. Scalable approaches to deal with such high-dimensional tasks experience a paradigm shift to address the unique modeling and computational challenges ...
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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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Panagiotis A. Traganitis2
Estimated H-index: 2
(University of Minnesota),
Georgios B. Giannakis113
Estimated H-index: 113
(University of Minnesota)
In recent years, advances in pattern recognition and data analytics have spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm exhibits different behavior for different types of data, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create such a high-performance meta-learner, by combining the outputs from multiple algorithms. The present work in...
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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...
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Yanning Shen4
Estimated H-index: 4
,
Geert Leus43
Estimated H-index: 43
,
Georgios B. Giannakis113
Estimated H-index: 113
Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender of users in a social network. However, real-world networks can have very large size, and nodal attributes can be unavailable to a number of nodes, e.g., due to privacy concerns. Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes. In this context, the present ...
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