Georgios B. Giannakis

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University of Minnesota

Endowed Chair Professor and Director at University of Minnesota

Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks

Uncovering anomalies in attributed networks has recently gained populari...
04/17/2021 ∙ by Konstantinos D. Polyzos, et al. ∙ 4 ∙ share

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Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks

To shift the computational burden from real-time to offline in delay-cri...
03/27/2021 ∙ by Manish K. Singh, et al. ∙ 10 ∙ share

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Bayesian Semi-supervised Crowdsourcing

Crowdsourcing has emerged as a powerful paradigm for efficiently labelin...
12/20/2020 ∙ by Panagiotis A. Traganitis, et al. ∙ 11 ∙ share

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Adversarial Linear Contextual Bandits with Graph-Structured Side Observations

This paper studies the adversarial graphical contextual bandits, a varia...
12/10/2020 ∙ by Lingda Wang, et al. ∙ 6 ∙ share

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Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem

Aiming at convex optimization under structural constraints, this work in...
12/09/2020 ∙ by Bingcong Li, et al. ∙ 6 ∙ share

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How Does Momentum Help Frank Wolfe?

We unveil the connections between Frank Wolfe (FW) type algorithms and t...
06/19/2020 ∙ by Bingcong Li, et al. ∙ 0 ∙ share

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Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets

This work investigates fault-resilient federated learning when the data ...
06/17/2020 ∙ by Yanjie Dong, et al. ∙ 11 ∙ share

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Tensor Graph Convolutional Networks for Multi-relational and Robust Learning

The era of "data deluge" has sparked renewed interest in graph-based lea...
03/15/2020 ∙ by Vassilis N. Ioannidis, et al. ∙ 48 ∙ share

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Efficient and Stable Graph Scattering Transforms via Pruning

Graph convolutional networks (GCNs) have well-documented performance in ...
01/27/2020 ∙ by Vassilis N. Ioannidis, et al. ∙ 0 ∙ share

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Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks

This paper deals with distributed finite-sum optimization for learning o...
12/29/2019 ∙ by Zhaoxian Wu, et al. ∙ 21 ∙ share

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Learning Connectivity and Higher-order Interactions in Radial Distribution Grids

To perform any meaningful optimization task, distribution grid operators...
11/08/2019 ∙ by Qiuling Yang, et al. ∙ 0 ∙ share

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Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation

Motivated by the emerging use of multi-agent reinforcement learning (MAR...
11/03/2019 ∙ by Jun Sun, et al. ∙ 18 ∙ share

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A Statistical Learning Approach to Reactive Power Control in Distribution Systems

Pronounced variability due to the growth of renewable energy sources, fl...
10/25/2019 ∙ by Qiuling Yang, et al. ∙ 5 ∙ share

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Edge Dithering for Robust Adaptive Graph Convolutional Networks

Graph convolutional networks (GCNs) are vulnerable to perturbations of t...
10/21/2019 ∙ by Vassilis N. Ioannidis, et al. ∙ 9 ∙ share

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GraphSAC: Detecting anomalies in large-scale graphs

A graph-based sampling and consensus (GraphSAC) approach is introduced t...
10/21/2019 ∙ by Vassilis N. Ioannidis, et al. ∙ 28 ∙ share

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Adaptive Step Sizes in Variance Reduction via Regularization

The main goal of this work is equipping convex and nonconvex problems wi...
10/15/2019 ∙ by Bingcong Li, et al. ∙ 30 ∙ share

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Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

The present paper develops a novel aggregated gradient approach for dist...
09/17/2019 ∙ by Jun Sun, et al. ∙ 1 ∙ share

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A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation

Motivated by the widespread use of temporal-difference (TD-) and Q-learn...
09/10/2019 ∙ by Gang Wang, et al. ∙ 13 ∙ share

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A Variational Bayes Approach to Adaptive Radio Tomography

Radio tomographic imaging (RTI) is an emerging technology for localizati...
09/05/2019 ∙ by Donghoon Lee, et al. ∙ 10 ∙ share

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Almost Tune-Free Variance Reduction

The variance reduction class of algorithms including the representative ...
08/25/2019 ∙ by Bingcong Li, et al. ∙ 2 ∙ share

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Unsupervised Ensemble Classification with Dependent Data

Ensemble learning, the machine learning paradigm where multiple algorith...
06/22/2019 ∙ by Panagiotis A. Traganitis, et al. ∙ 7 ∙ share

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Generalization error bounds for kernel matrix completion and extrapolation

Prior information can be incorporated in matrix completion to improve es...
06/20/2019 ∙ by Pere Giménez-Febrer, et al. ∙ 4 ∙ share

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On the Convergence of SARAH and Beyond

The main theme of this work is a unifying algorithm, abbreviated as L2S,...
06/05/2019 ∙ by Bingcong Li, et al. ∙ 1 ∙ share

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Tight Linear Convergence Rate of ADMM for Decentralized Optimization

The present paper considers leveraging network topology information to i...
05/24/2019 ∙ by Meng Ma, et al. ∙ 0 ∙ share

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Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks

Despite their unprecedented performance in various domains, utilization ...
04/05/2019 ∙ by Fatemeh Sheikholeslami, et al. ∙ 0 ∙ share

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Adaptive Caching via Deep Reinforcement Learning

Caching is envisioned to play a critical role in next-generation content...
02/27/2019 ∙ by Alireza Sadeghi, et al. ∙ 0 ∙ share

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Distributed Network Caching via Dynamic Programming

Next-generation communication networks are envisioned to extensively uti...
02/19/2019 ∙ by Alireza Sadeghi, et al. ∙ 0 ∙ share

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Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing

Small base stations (SBs) of fifth-generation (5G) cellular networks are...
12/17/2018 ∙ by Alireza Sadeghi, et al. ∙ 0 ∙ share

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Communication-Efficient Distributed Reinforcement Learning

This paper studies the distributed reinforcement learning (DRL) problem ...
12/07/2018 ∙ by Tianyi Chen, et al. ∙ 0 ∙ share

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Online Graph-Adaptive Learning with Scalability and Privacy

Graphs are widely adopted for modeling complex systems, including financ...
12/03/2018 ∙ by Yanning Shen, et al. ∙ 0 ∙ share

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Graph Multiview Canonical Correlation Analysis

Multiview canonical correlation analysis (MCCA) seeks latent low-dimensi...
11/29/2018 ∙ by Jia Chen, et al. ∙ 0 ∙ share

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Adaptive-similarity node embedding for scalable learning over graphs

Node embedding is the task of extracting informative and descriptive fea...
11/27/2018 ∙ by Dimitris Berberidis, et al. ∙ 0 ∙ share

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Real-time Power System State Estimation and Forecasting via Deep Neural Networks

Contemporary smart power grids are being challenged by rapid voltage flu...
11/15/2018 ∙ by Liang Zhang, et al. ∙ 0 ∙ share

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RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets

In this paper, we propose a class of robust stochastic subgradient metho...
11/09/2018 ∙ by Liping Li, et al. ∙ 0 ∙ share

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A Recurrent Graph Neural Network for Multi-Relational Data

The era of data deluge has sparked the interest in graph-based learning ...
11/05/2018 ∙ by Vassilis N. Ioannidis, et al. ∙ 0 ∙ share

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Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of netw...
10/27/2018 ∙ by Tianyi Chen, et al. ∙ 0 ∙ share

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Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection

Joint analysis of data from multiple information repositories facilitate...
09/22/2018 ∙ by Vassilis N. Ioannidis, et al. ∙ 0 ∙ share

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Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization

Neural networks with ReLU activations have achieved great empirical succ...
08/14/2018 ∙ by Gang Wang, et al. ∙ 6 ∙ share

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Matrix completion and extrapolation via kernel regression

Matrix completion and extrapolation (MCEX) are dealt with here over repr...
08/01/2018 ∙ by Pere Giménez-Febrer, et al. ∙ 0 ∙ share

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Delayed Bandit Online Learning with Unknown Delays

This paper studies bandit learning problems with delayed feedback, which...
07/09/2018 ∙ by Bingcong Li, et al. ∙ 2 ∙ share

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LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

This paper presents a new class of gradient methods for distributed mach...
05/25/2018 ∙ by Tianyi Chen, et al. ∙ 0 ∙ share

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Semi-Blind Inference of Topologies and Dynamical Processes over Graphs

Network science provides valuable insights across numerous disciplines i...
05/16/2018 ∙ by Vassilis N. Ioannidis, et al. ∙ 0 ∙ share

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Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets

Principal component analysis (PCA) is widely used for feature extraction...
05/15/2018 ∙ by Jia Chen, et al. ∙ 0 ∙ share

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Secure Mobile Edge Computing in IoT via Collaborative Online Learning

To accommodate heterogeneous tasks in Internet of Things (IoT), a new co...
05/09/2018 ∙ by Bingcong Li, et al. ∙ 0 ∙ share

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Multi-Timescale Online Optimization of Network Function Virtualization for Service Chaining

Network Function Virtualization (NFV) can cost-efficiently provide netwo...
04/19/2018 ∙ by Xiaojing Chen, et al. ∙ 0 ∙ share

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Fast Decentralized Optimization over Networks

The present work introduces the hybrid consensus alternating direction m...
04/06/2018 ∙ by Meng Ma, et al. ∙ 0 ∙ share

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Adaptive Bayesian Radio Tomography

Radio tomographic imaging (RTI) is an emerging technology to locate phys...
04/06/2018 ∙ by Donghoon Lee, et al. ∙ 0 ∙ share

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Adaptive Diffusions for Scalable Learning over Graphs

Diffusion-based classifiers such as those relying on the Personalized Pa...
04/05/2018 ∙ by Dimitris Berberidis, et al. ∙ 0 ∙ share

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Canonical Correlation Analysis of Datasets with a Common Source Graph

Canonical correlation analysis (CCA) is a powerful technique for discove...
03/27/2018 ∙ by Jia Chen, et al. ∙ 0 ∙ share

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Nonlinear Dimensionality Reduction on Graphs

In this era of data deluge, many signal processing and machine learning ...
01/29/2018 ∙ by Yanning Shen, et al. ∙ 0 ∙ share

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Georgios B. Giannakis

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