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08/13/2023
Conic Descent Redux for Memory-Efficient Optimization
Conic programming has well-documented merits in a gamut of signal proces...
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03/31/2023
Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
Meta-learning owns unique effectiveness and swiftness in tackling emergi...
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11/16/2022
Bayesian Optimization for Online Management in Dynamic Mobile Edge Computing
Recent years have witnessed the emergence of mobile edge computing (MEC)...
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06/10/2022
Weighted Ensembles for Active Learning with Adaptivity
Labeled data can be expensive to acquire in several application domains,...
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05/27/2022
Surrogate modeling for Bayesian optimization beyond a single Gaussian process
Bayesian optimization (BO) has well-documented merits for optimizing bla...
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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...
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10/20/2021
Distributionally Robust Semi-Supervised Learning Over Graphs
Semi-supervised learning (SSL) over graph-structured data emerges in man...
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10/13/2021
Incremental Ensemble Gaussian Processes
Belonging to the family of Bayesian nonparametrics, Gaussian process (GP...
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10/08/2021
Heavy Ball Momentum for Conditional Gradient
Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documen...
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10/07/2021
Detecting adversaries in Crowdsourcing
Despite its successes in various machine learning and data science tasks...
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04/17/2021
Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks
Uncovering anomalies in attributed networks has recently gained populari...
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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...
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12/20/2020
Bayesian Semi-supervised Crowdsourcing
Crowdsourcing has emerged as a powerful paradigm for efficiently labelin...
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12/10/2020
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations
This paper studies the adversarial graphical contextual bandits, a varia...
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12/09/2020
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem
Aiming at convex optimization under structural constraints, this work in...
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06/19/2020
How Does Momentum Help Frank Wolfe?
We unveil the connections between Frank Wolfe (FW) type algorithms and t...
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06/17/2020
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
This work investigates fault-resilient federated learning when the data ...
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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...
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01/27/2020
Efficient and Stable Graph Scattering Transforms via Pruning
Graph convolutional networks (GCNs) have well-documented performance in ...
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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...
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11/08/2019
Learning Connectivity and Higher-order Interactions in Radial Distribution Grids
To perform any meaningful optimization task, distribution grid operators...
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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...
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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...
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10/21/2019
Edge Dithering for Robust Adaptive Graph Convolutional Networks
Graph convolutional networks (GCNs) are vulnerable to perturbations of t...
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10/21/2019
GraphSAC: Detecting anomalies in large-scale graphs
A graph-based sampling and consensus (GraphSAC) approach is introduced t...
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10/15/2019
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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09/17/2019
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
The present paper develops a novel aggregated gradient approach for dist...
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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...
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09/05/2019
A Variational Bayes Approach to Adaptive Radio Tomography
Radio tomographic imaging (RTI) is an emerging technology for localizati...
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08/25/2019
Almost Tune-Free Variance Reduction
The variance reduction class of algorithms including the representative ...
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06/22/2019
Unsupervised Ensemble Classification with Dependent Data
Ensemble learning, the machine learning paradigm where multiple algorith...
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06/20/2019
Generalization error bounds for kernel matrix completion and extrapolation
Prior information can be incorporated in matrix completion to improve es...
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06/05/2019
On the Convergence of SARAH and Beyond
The main theme of this work is a unifying algorithm, abbreviated as L2S,...
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05/24/2019
Tight Linear Convergence Rate of ADMM for Decentralized Optimization
The present paper considers leveraging network topology information to i...
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04/05/2019
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
Despite their unprecedented performance in various domains, utilization ...
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02/27/2019
Adaptive Caching via Deep Reinforcement Learning
Caching is envisioned to play a critical role in next-generation content...
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02/19/2019
Distributed Network Caching via Dynamic Programming
Next-generation communication networks are envisioned to extensively uti...
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12/17/2018
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing
Small base stations (SBs) of fifth-generation (5G) cellular networks are...
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12/07/2018
Communication-Efficient Distributed Reinforcement Learning
This paper studies the distributed reinforcement learning (DRL) problem ...
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12/03/2018
Online Graph-Adaptive Learning with Scalability and Privacy
Graphs are widely adopted for modeling complex systems, including financ...
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11/29/2018
Graph Multiview Canonical Correlation Analysis
Multiview canonical correlation analysis (MCCA) seeks latent low-dimensi...
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11/27/2018
Adaptive-similarity node embedding for scalable learning over graphs
Node embedding is the task of extracting informative and descriptive fea...
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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...
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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...
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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 ...
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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...
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09/22/2018
Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
Joint analysis of data from multiple information repositories facilitate...
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08/14/2018
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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08/01/2018
Matrix completion and extrapolation via kernel regression
Matrix completion and extrapolation (MCEX) are dealt with here over repr...
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07/09/2018
