71 accepted papers
Meta-learning offers a principled framework leveraging *task-invariant* priors from related tasks, with which *task-specific* models can be fine-tuned on downstream tasks, even with limited data records. Gradient-based meta-learning (GBML) relies on gradient descent (GD) to adapt the prior to a new…
Uncertainty quantification (UQ) over graphs arises in a number of safety-critical applications in network science. The Gaussian process (GP), as a classical Bayesian framework for UQ, has been developed to handle graph-structured data by devising topology-aware kernel functions. However, such GP-bas…
3D reconstruction is a fundamental task in robotics that has gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. While this task can be carried out using a large number of arbitrarily taken 2D images, their proc…
As large language models (LLMs) continue to scale in size, the computational overhead has become a major bottleneck for task-specific fine-tuning. While low-rank adaptation (LoRA) effectively curtails this cost by confining the weight updates to a low-dimensional subspace, such a restriction can hin…
3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. While this task can be carried out using a large number of arbitrarily taken 2D images, their processi
The Gaussian process (GP) is a Bayesian non-parametric paradigm that is widely adopted for uncertainty quantification (UQ) in a number of safety-critical applications, including robotics, healthcare, as well as surveillance. The consistency of the resulting uncertainty values however, hinges on the…
Targeting solutions over ‘flat’ regions of the loss landscape, sharpness-aware minimization (SAM) has emerged as a powerful tool to improve generalizability of deep neural network based learning. While several SAM variants have been developed to this end, a unifying approach that also guides princip…
Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanc…
Using a subset of observed network links, high-order link prediction (HOLP) infers missing hyperedges, that is links connecting three or more nodes. HOLP emerges in several applications, but existing approaches have not dealt with the associated predictor’s performance. To overcome this limitation,…
Relying on prior knowledge accumulated from related tasks, meta-learning offers a powerful approach to learning a novel task from a limited number of training data. Recent approaches use a family of prior probability density functions or recurrent neural network models, whose parameters can be optim…
Meta-learning empowers data-hungry deep neural networks to rapidly learn from merely a few samples, which is especially appealing to tasks with small datasets. Critical in this context is the *prior knowledge* accumulated from related tasks. Existing meta-learning approaches typically rely on presel…
Utilizing task-invariant prior knowledge extracted from related tasks, meta-learning is a principled framework that empowers learning a new task especially when data records are limited. A fundamental challenge in meta-learning is how to quickly "adapt" the extracted prior in order to train a task-s…
Optimizing a black-box function that is expensive to evaluate emerges in a gamut of machine learning and artificial intelligence applications including drug discovery, policy optimization in robotics, and hyperparameter tuning of learning models to list a few. Bayesian optimization (BO) provides a p…
Sharpness-aware minimization (SAM) has well documented merits in enhancing generalization of deep neural networks, even without sizable data augmentation. Embracing the geometry of the loss function, where neighborhoods of 'flat minima' heighten generalization ability, SAM seeks 'flat valleys' by mi…
Higher-order link prediction (HOLP) seeks missing links capturing dependencies among three or more network nodes. Predicting high-order links (HOLs) can for instance reveal hyperlinks in the structure of drug substance and metabolic networks. Existing methods either make restrictive assumptions rega…
Assessing set-membership and evaluating distances to the related set boundary are problems of widespread interest, and can often be computationally challenging. Seeking efficient learning models for such tasks, this paper deals with voltage stability margin prediction for power systems. Supervised t…
Meta-learning owns unique effectiveness and swiftness in tackling emerging tasks with limited data. Its broad applicability is revealed by viewing it as a bi-level optimization problem. The resultant algorithmic viewpoint however, faces scalability issues when the inner-level optimization relies on…
Relying on a conditional gradient based iteration, the Frank-Wolfe (FW) algorithm has been a popular solver of constrained convex optimization problems in signal processing and machine learning, thanks to its low complexity. The present contribution broadens its scope by replacing the gradient per F…
This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: contexts and side observations. In this setting, a learning agent repeatedly chooses from a set of K actions after being pre…
Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of ExtraFW is the pair of gradients leveraged per iteration, thanks to which the decision variable is updated in a prediction…
Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous. The present paper revisits policy evaluation via temporal-difference (TD) learning from the Gaussian process (GP) perspective. Leveraging random features to a…
Graph-guided semi-supervised learning (SSL) is a major task emerging in a gamut of network science applications. However, most SSL approaches rely on deterministic similarity metrics for prediction, thus providing only point estimates of the sought function. To allow for uncertainty quantification,…
Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented merits in machine learning and signal processing applications. Unlike projection-based methods, momentum cannot improve the convergence rate of FW, in general. This limitation motivates the present work, which deals with hea…
The present work addresses the problem of adversarial attacks in unsupervised ensemble or crowdsourcing classification tasks. Under certain conditions, it is shown, both analytically and through numerical tests, that spammers cause the most damage with respect to classification performance. To curb…
Gaussian process latent variable models (GPLVMs) are powerful, yet computationally heavy tools for nonlinear dimensionality reduction. Existing scalable variants utilize low- rank kernel matrix approximants that in essence subsample the embedding space. This work develops an efficient online approac…
The present paper develops a graph-based sampling and consensus (GraphSAC) approach to effectively detect anomalous nodes in large-scale graphs. GraphSAC randomly draws sub-sets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employ…
The present work introduces a simple scheme for active classification of data using unsupervised ensembles of classifiers. Uncertainty sampling, with different uncertainty measures, is evaluated for data selection, while an online expectation maximization algorithm is derived to estimate model param…
The variance reduction class of algorithms including the representative ones, SVRG and SARAH, have well documented merits for empirical risk minimization problems. However, they require grid search to tune parameters (step size and the number of iterations per inner loop) for optimal performance. Th…
The interconnection of social, email, and media platforms enables adversaries to manipulate networked data and promote their malicious intents. This paper introduces graph neural network architectures that are robust to perturbed networked data. The novel network utilizes a randomization layer that…
Combining benefits of kernels with Bayesian models, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also quantifying the associated uncertainty. While most GP approaches rely on a single preselected prior, the pres…
Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear…
Inspired by the widespread use of Q-learning algorithms in reinforcement learning (RL), this present paper studies a class of biased stochastic approximation (SA) procedures under an ‘ergodic-like’ assumption on the underlying stochastic noise sequence. Leveraging a \emph{multistep Lyapunov functio…
Wireless and wireline networks, such as Internet, cellular, and content delivery networks are to serve end-user file requests proactively. To this aim, by storing anticipated highly popular files during off-peak periods, and fetching them to end-users during on-peak instances, these networks smoothe…
To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactio…
The main theme of this work is a unifying algorithm, \textbf{L}oop\textbf{L}ess \textbf{S}ARAH (L2S) for problems formulated as summation of $n$ individual loss functions. L2S broadens a recently developed variance reduction method known as SARAH. To find an $\epsilon$-accurate solution, L2S enjoys…
In this work, we consider the distributed optimization problem using networked computing machines. Specifically, we are interested in solving this problem using the alternating direction method of multipliers (ADMM) while accounting for edge weights. Existing works focus on star graphs and use simpl…
Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks. However, theoretical analysis of such approaches is still at its infancy. Graph scattering transforms (GSTs) are non-trainable deep GCN models that are amenable to generalization…
This contribution deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable…
In this paper, we revisit the problem of minimizing a convex function f(x) with Lipschitz continuous gradient via accelerated gradient methods (AGM). To do so, we consider the so-called estimate sequence (ES), a useful analysis tool for establishing the convergence of AGM. We develop a generalized E…
Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Sever…
Semi-supervised learning (SSL) of dynamic processes over graphs is encountered in several applications of network science. Most of the existing approaches are unable to handle graphs with multiple relations, which arise in various real-world networks. This work deals with SSL of dynamic processes ov…
The era of "data deluge" has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering. In this paper, we introduce a graph recurrent neural network (GRNN) for scalable semi-supervised learning from multi-relational data.…
Channel-gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. State-of-the-art on this subject includes tomography-based approaches, where shadowing effects are modeled by the weighted integral of a spatia…
This paper deals with bandit online learning, where feedback of unknown delay can emerge in non-stochastic multi-armed bandit (MAB) and bandit convex optimization (BCO) settings. MAB and BCO require only values of the objective function to become available through feedback, and are used to estimate…
Next-generation communication networks are envisioned to extensively utilize storage-enabled caching units to alleviate unfavorable surges of data traffic by pro-actively storing anticipated highly popular contents across geographically distributed storage devices during off-peak periods. This resou…
Despite their well-documented learning capabilities in clean environments, deep convolutional neural networks (CNNs) are extremely fragile in adversarial settings, where carefully crafted perturbations created by an attacker can easily disrupt the task at hand. Numerous methods have been proposed fo…
Multiview canonical correlation analysis (MCCA) looks for shared low-dimensional representations hidden in multiple transformations of common source signals. Existing MCCA approaches do not exploit the geometry of common sources, which can be either given a priori, or constructed from do- main knowl…
State forecasting plays a critical role in power system monitoring, by offering system awareness even ahead of the time horizon, enhancing system observability, and providing efficient identification of the grid topology and link parameter changes. However, available approaches relying on linear est…
Channel gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the p…
Principal component analysis (PCA) has well-documented merits for data extraction and dimensionality reduction. PCA deals with a single dataset at a time, and it is challenged when it comes to analyzing multiple datasets. Yet in certain setups, one wishes to extract the most significant information…
The Alternating Directions Methods of Multipliers (ADMM) has witnessed a resurgence of interest over the past few years fueled by the ever increasing demand for scalable optimization techniques to tackle real-world statistical learning problems. However, despite its success in several application se…
Segmentation of ventricles from cardiac magnetic resonance (MR) images is a key step to obtaining clinical parameters useful for prognosis of cardiac pathologies. To improve upon the performance of existing fully convolutional network (FCN) based automatic right ventricle (RV) segmentation approache…
This paper focuses on the online fog computing tasks in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems wh…
Kernel-based methods have well-appreciated performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. To cope with this limitation, multi-kernel learning has gained popularity thanks to its flexibility in…
The present work introduces methods for sampling and inference for the purpose of semi-supervised classification over the nodes of a graph. The graph may be given or constructed using similarity measures among nodal features. Leveraging the graph for classification builds on the premise that relatio…
In next generation cellular networks (5G) the access points (APs) are anticipated to be equipped with storage devices to serve locally requests for reusable popular contents by caching them at the edge of the network. The ultimate goal is to shift part of the load on the back-haul links from on-peak…
The deluge of networked big data motivates the development of computation- and communication-efficient network information processing algorithms. In this paper, we propose two data-adaptive censoring strategies that significantly reduce the computation and communication costs of the distributed recu…
Granger causality based approaches are popular in unveiling directed interactions among brain regions. The present work advocates a multi-kernel based nonlinear model for obtaining the effective connectivity between brain regions, by wedding the merits of partial correlation in undirected topology i…
Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all ann…
A linear-time algorithm termed SPARse Truncated Amplitude flow (SPARTA) is developed for the phase retrieval (PR) of sparse signals. Upon formulating the sparse PR as a non-convex empirical loss minimization task, SPARTA emerges as an iterative solver consisting of two components: s1) a sparse ortho…
Linear structural vector autoregressive models constitute a generalization of structural equation models (SEMs) and vector autoregressive (VAR) models, two popular approaches for topology inference of directed graphs. Although simple and tractable, linear SVARMs seldom capture nonlinearities that ar…
In an age of exponentially increasing data generation, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper puts forth approaches to render tracking of large-scale dynamic processes affordable, by processing a reduced…
We study the problem of high-dimensional covariance matrix estimation from partial observations. We consider covariance matrices modeled as Kronecker products of matrix factors, and rely on observations with missing values. In the absence of missing data, observation vectors are assumed to be i.i.d…
Partial correlations (PCs) of functional magnetic resonance imaging (fMRI) time series play a principal role in revealing connectivity of brain networks. To explore nonlinear behavior of the blood-oxygen-level dependent signal, the present work postulates a kernel-based nonlinear connectivity model…
Big data applications demand efficient solvers capable of providing accurate solutions to large-scale problems at affordable computational costs. Processing data sequentially, online algorithms offer attractive means to deal with massive data sets. However, they may incur prohibitive complexity in h…
A systematic framework is put forth in this paper to integrate renewable energy sources (RES), distributed storage units, cooling facilities, as well as dynamic pricing into the workload and energy management tasks for a data center network. To cope with RES uncertainty, the resource allocation task…
Variabilities of renewable energy sources critically challenge contemporary power distribution grids. Depending on grid conditions, solar energy may have to be curtailed to comply with network limitations. On the other hand, smart inverters installed with solar panels enable for reactive power suppo…
An infinite time-horizon resource allocation problem is formulated to maximize the time-averaged multi-input multi-output (MIMO) downlink throughput, subject to a time-averaged energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient based online control (S…
Albeit being in the big data era, a significant percentage of data accrued can be overlooked while maintaining reasonable quality of statistical inference at affordable complexity. By capitalizing on data redundancy, interval censoring is leveraged here to cope with the scarcity of resources needed…
Complex phenomena involving pairwise interactions in natural and man-made settings can be well-represented by networks. Besides statistical and computational analyses on such networks, visualization plays a crucial role towards effectively conveying “at-a-glance” structural properties such as node h…
This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by low-complexity sensing hardware and stringent communication constraints, compressed and quantized measurements are considered.…