
Bayesian Semisupervised Crowdsourcing
Crowdsourcing has emerged as a powerful paradigm for efficiently labelin...
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Adversarial Linear Contextual Bandits with GraphStructured Side Observations
This paper studies the adversarial graphical contextual bandits, a varia...
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Enhancing ParameterFree Frank Wolfe with an Extra Subproblem
Aiming at convex optimization under structural constraints, this work in...
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How Does Momentum Help Frank Wolfe?
We unveil the connections between Frank Wolfe (FW) type algorithms and t...
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CommunicationEfficient Robust Federated Learning Over Heterogeneous Datasets
This work investigates faultresilient federated learning when the data ...
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Tensor Graph Convolutional Networks for Multirelational and Robust Learning
The era of "data deluge" has sparked renewed interest in graphbased lea...
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Efficient and Stable Graph Scattering Transforms via Pruning
Graph convolutional networks (GCNs) have welldocumented performance in ...
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Federated VarianceReduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
This paper deals with distributed finitesum optimization for learning o...
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Learning Connectivity and Higherorder Interactions in Radial Distribution Grids
To perform any meaningful optimization task, distribution grid operators...
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FiniteSample Analysis of Decentralized TemporalDifference Learning with Linear Function Approximation
Motivated by the emerging use of multiagent reinforcement learning (MAR...
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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...
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Edge Dithering for Robust Adaptive Graph Convolutional Networks
Graph convolutional networks (GCNs) are vulnerable to perturbations of t...
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GraphSAC: Detecting anomalies in largescale graphs
A graphbased sampling and consensus (GraphSAC) approach is introduced t...
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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...
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CommunicationEfficient Distributed Learning via Lazily Aggregated Quantized Gradients
The present paper develops a novel aggregated gradient approach for dist...
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A Multistep Lyapunov Approach for FiniteTime Analysis of Biased Stochastic Approximation
Motivated by the widespread use of temporaldifference (TD) and Qlearn...
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A Variational Bayes Approach to Adaptive Radio Tomography
Radio tomographic imaging (RTI) is an emerging technology for localizati...
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Almost TuneFree Variance Reduction
The variance reduction class of algorithms including the representative ...
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Unsupervised Ensemble Classification with Dependent Data
Ensemble learning, the machine learning paradigm where multiple algorith...
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Generalization error bounds for kernel matrix completion and extrapolation
Prior information can be incorporated in matrix completion to improve es...
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On the Convergence of SARAH and Beyond
The main theme of this work is a unifying algorithm, abbreviated as L2S,...
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Tight Linear Convergence Rate of ADMM for Decentralized Optimization
The present paper considers leveraging network topology information to i...
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Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
Despite their unprecedented performance in various domains, utilization ...
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Adaptive Caching via Deep Reinforcement Learning
Caching is envisioned to play a critical role in nextgeneration content...
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Distributed Network Caching via Dynamic Programming
Nextgeneration communication networks are envisioned to extensively uti...
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Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing
Small base stations (SBs) of fifthgeneration (5G) cellular networks are...
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CommunicationEfficient Distributed Reinforcement Learning
This paper studies the distributed reinforcement learning (DRL) problem ...
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Online GraphAdaptive Learning with Scalability and Privacy
Graphs are widely adopted for modeling complex systems, including financ...
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Graph Multiview Canonical Correlation Analysis
Multiview canonical correlation analysis (MCCA) seeks latent lowdimensi...
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Adaptivesimilarity node embedding for scalable learning over graphs
Node embedding is the task of extracting informative and descriptive fea...
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Realtime Power System State Estimation and Forecasting via Deep Neural Networks
Contemporary smart power grids are being challenged by rapid voltage flu...
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RSA: ByzantineRobust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
In this paper, we propose a class of robust stochastic subgradient metho...
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A Recurrent Graph Neural Network for MultiRelational Data
The era of data deluge has sparked the interest in graphbased learning ...
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Learning and Management for InternetofThings: Accounting for Adaptivity and Scalability
InternetofThings (IoT) envisions an intelligent infrastructure of netw...
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Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
Joint analysis of data from multiple information repositories facilitate...
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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...
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Matrix completion and extrapolation via kernel regression
Matrix completion and extrapolation (MCEX) are dealt with here over repr...
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Delayed Bandit Online Learning with Unknown Delays
This paper studies bandit learning problems with delayed feedback, which...
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LAG: Lazily Aggregated Gradient for CommunicationEfficient Distributed Learning
This paper presents a new class of gradient methods for distributed mach...
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SemiBlind Inference of Topologies and Dynamical Processes over Graphs
Network science provides valuable insights across numerous disciplines i...
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Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets
Principal component analysis (PCA) is widely used for feature extraction...
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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...
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MultiTimescale Online Optimization of Network Function Virtualization for Service Chaining
Network Function Virtualization (NFV) can costefficiently provide netwo...
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Fast Decentralized Optimization over Networks
The present work introduces the hybrid consensus alternating direction m...
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Adaptive Bayesian Radio Tomography
Radio tomographic imaging (RTI) is an emerging technology to locate phys...
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Adaptive Diffusions for Scalable Learning over Graphs
Diffusionbased classifiers such as those relying on the Personalized Pa...
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Canonical Correlation Analysis of Datasets with a Common Source Graph
Canonical correlation analysis (CCA) is a powerful technique for discove...
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Nonlinear Dimensionality Reduction on Graphs
In this era of data deluge, many signal processing and machine learning ...
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Random Featurebased Online Multikernel Learning in Environments with Unknown Dynamics
Kernelbased methods exhibit welldocumented performance in various nonl...
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Online Ensemble Multikernel Learning Adaptive to Nonstationary and Adversarial Environments
Kernelbased methods exhibit welldocumented performance in various nonl...
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Georgios B. Giannakis
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Endowed Chair Professor and Director at University of Minnesota