Georgios B. Giannakis

research

∙ 06/10/2022

Weighted Ensembles for Active Learning with Adaptivity

Labeled data can be expensive to acquire in several application domains,...

Konstantinos D. Polyzos, et al. ∙

research

∙ 05/27/2022

Surrogate modeling for Bayesian optimization beyond a single Gaussian process

Bayesian optimization (BO) has well-documented merits for optimizing bla...

Qin Lu, et al. ∙

research

∙ 12/01/2021

Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes

Value function approximation is a crucial module for policy evaluation i...

Qin Lu, et al. ∙

research

∙ 10/20/2021

Distributionally Robust Semi-Supervised Learning Over Graphs

Semi-supervised learning (SSL) over graph-structured data emerges in man...

Alireza Sadeghi, et al. ∙

research

∙ 10/13/2021

Incremental Ensemble Gaussian Processes

Belonging to the family of Bayesian nonparametrics, Gaussian process (GP...

Qin Lu, et al. ∙

research

∙ 10/08/2021

Heavy Ball Momentum for Conditional Gradient

Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documen...

Bingcong Li, et al. ∙

research

∙ 10/07/2021

Detecting adversaries in Crowdsourcing

Despite its successes in various machine learning and data science tasks...

Panagiotis A. Traganitis, et al. ∙

research

∙ 04/17/2021

Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks

Uncovering anomalies in attributed networks has recently gained populari...

Konstantinos D. Polyzos, et al. ∙

research

∙ 03/27/2021

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...

Manish K. Singh, et al. ∙

research

∙ 12/20/2020

Bayesian Semi-supervised Crowdsourcing

Crowdsourcing has emerged as a powerful paradigm for efficiently labelin...

Panagiotis A. Traganitis, et al. ∙

research

∙ 12/10/2020

Adversarial Linear Contextual Bandits with Graph-Structured Side Observations

This paper studies the adversarial graphical contextual bandits, a varia...

Lingda Wang, et al. ∙

research

∙ 12/09/2020

Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem

Aiming at convex optimization under structural constraints, this work in...

Bingcong Li, et al. ∙

research

∙ 06/19/2020

How Does Momentum Help Frank Wolfe?

We unveil the connections between Frank Wolfe (FW) type algorithms and t...

Bingcong Li, et al. ∙

research

∙ 06/17/2020

Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets

This work investigates fault-resilient federated learning when the data ...

Yanjie Dong, et al. ∙

research

∙ 03/15/2020

Tensor Graph Convolutional Networks for Multi-relational and Robust Learning

The era of "data deluge" has sparked renewed interest in graph-based lea...

Vassilis N. Ioannidis, et al. ∙

research

∙ 01/27/2020

Efficient and Stable Graph Scattering Transforms via Pruning

Graph convolutional networks (GCNs) have well-documented performance in ...

Vassilis N. Ioannidis, et al. ∙

research

∙ 12/29/2019

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

This paper deals with distributed finite-sum optimization for learning o...

Zhaoxian Wu, et al. ∙

research

∙ 11/08/2019

Learning Connectivity and Higher-order Interactions in Radial Distribution Grids

To perform any meaningful optimization task, distribution grid operators...

Qiuling Yang, et al. ∙

research

∙ 11/03/2019

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

Motivated by the emerging use of multi-agent reinforcement learning (MAR...

Jun Sun, et al. ∙

research

∙ 10/25/2019

A Statistical Learning Approach to Reactive Power Control in Distribution Systems

Pronounced variability due to the growth of renewable energy sources, fl...

Qiuling Yang, et al. ∙

research

∙ 10/21/2019

Edge Dithering for Robust Adaptive Graph Convolutional Networks

Graph convolutional networks (GCNs) are vulnerable to perturbations of t...

Vassilis N. Ioannidis, et al. ∙

research

∙ 10/21/2019

GraphSAC: Detecting anomalies in large-scale graphs

A graph-based sampling and consensus (GraphSAC) approach is introduced t...

Vassilis N. Ioannidis, et al. ∙

research

∙ 10/15/2019

Adaptive Step Sizes in Variance Reduction via Regularization

The main goal of this work is equipping convex and nonconvex problems wi...

Bingcong Li, et al. ∙

research

∙ 09/17/2019

Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

The present paper develops a novel aggregated gradient approach for dist...

Jun Sun, et al. ∙

research

∙ 09/10/2019

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

Motivated by the widespread use of temporal-difference (TD-) and Q-learn...

Gang Wang, et al. ∙

research

∙ 09/05/2019

A Variational Bayes Approach to Adaptive Radio Tomography

Radio tomographic imaging (RTI) is an emerging technology for localizati...

Donghoon Lee, et al. ∙

research

∙ 08/25/2019

Almost Tune-Free Variance Reduction

The variance reduction class of algorithms including the representative ...

Bingcong Li, et al. ∙

research

∙ 06/22/2019

Unsupervised Ensemble Classification with Dependent Data

Ensemble learning, the machine learning paradigm where multiple algorith...

Panagiotis A. Traganitis, et al. ∙

research

∙ 06/20/2019

Generalization error bounds for kernel matrix completion and extrapolation

Prior information can be incorporated in matrix completion to improve es...

Pere Giménez-Febrer, et al. ∙

research

∙ 06/05/2019

On the Convergence of SARAH and Beyond

The main theme of this work is a unifying algorithm, abbreviated as L2S,...

Bingcong Li, et al. ∙

research

∙ 05/24/2019

Tight Linear Convergence Rate of ADMM for Decentralized Optimization

The present paper considers leveraging network topology information to i...

Meng Ma, et al. ∙

research

∙ 04/05/2019

Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks

Despite their unprecedented performance in various domains, utilization ...

Fatemeh Sheikholeslami, et al. ∙

research

∙ 02/27/2019

Adaptive Caching via Deep Reinforcement Learning

Caching is envisioned to play a critical role in next-generation content...

Alireza Sadeghi, et al. ∙

research

∙ 02/19/2019

Distributed Network Caching via Dynamic Programming

Next-generation communication networks are envisioned to extensively uti...

Alireza Sadeghi, et al. ∙

research

∙ 12/17/2018

Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing

Small base stations (SBs) of fifth-generation (5G) cellular networks are...

Alireza Sadeghi, et al. ∙

research

∙ 12/07/2018

Communication-Efficient Distributed Reinforcement Learning

This paper studies the distributed reinforcement learning (DRL) problem ...

Tianyi Chen, et al. ∙

research

∙ 12/03/2018

Online Graph-Adaptive Learning with Scalability and Privacy

Graphs are widely adopted for modeling complex systems, including financ...

Yanning Shen, et al. ∙

research

∙ 11/29/2018

Graph Multiview Canonical Correlation Analysis

Multiview canonical correlation analysis (MCCA) seeks latent low-dimensi...

Jia Chen, et al. ∙

research

∙ 11/27/2018

Adaptive-similarity node embedding for scalable learning over graphs

Node embedding is the task of extracting informative and descriptive fea...

Dimitris Berberidis, et al. ∙

research

∙ 11/15/2018

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

Contemporary smart power grids are being challenged by rapid voltage flu...

Liang Zhang, et al. ∙

research

∙ 11/09/2018

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

In this paper, we propose a class of robust stochastic subgradient metho...

Liping Li, et al. ∙

research

∙ 11/05/2018

A Recurrent Graph Neural Network for Multi-Relational Data

The era of data deluge has sparked the interest in graph-based learning ...

Vassilis N. Ioannidis, et al. ∙

research

∙ 10/27/2018

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

Internet-of-Things (IoT) envisions an intelligent infrastructure of netw...

Tianyi Chen, et al. ∙

research

∙ 09/22/2018

Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection

Joint analysis of data from multiple information repositories facilitate...

Vassilis N. Ioannidis, et al. ∙

research

∙ 08/14/2018

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

Neural networks with ReLU activations have achieved great empirical succ...

Gang Wang, et al. ∙

research

∙ 08/01/2018

Matrix completion and extrapolation via kernel regression

Matrix completion and extrapolation (MCEX) are dealt with here over repr...

Pere Giménez-Febrer, et al. ∙

research

∙ 07/09/2018

Delayed Bandit Online Learning with Unknown Delays

This paper studies bandit learning problems with delayed feedback, which...

Bingcong Li, et al. ∙

research

∙ 05/25/2018

LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

This paper presents a new class of gradient methods for distributed mach...

Tianyi Chen, et al. ∙

research

∙ 05/16/2018

Semi-Blind Inference of Topologies and Dynamical Processes over Graphs

Network science provides valuable insights across numerous disciplines i...

Vassilis N. Ioannidis, et al. ∙

research

∙ 05/15/2018

Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets

Principal component analysis (PCA) is widely used for feature extraction...

Jia Chen, et al. ∙

Georgios B. Giannakis

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