26 accepted papers
Trustworthy decision making in networked, dynamic environments calls for innovative uncertainty quantification substrates in predictive models for graph time series. Existing conformal prediction (CP) methods have been applied separately to multivariate time series and static graphs, but they either…
Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a prominent example, Principal Component Analysis (PCA) projects data points onto the eigenvectors of their covariance matr
Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key challenge while learning representations, wherein it is important to retain and update information over long temporal h…
Decentralized learning has emerged as a popular method due to its excellent scalability and parallel implementation of stochastic gradient methods. However, the main challenges in decentralized learning include the communication overhead and privacy concerns associated with sharing gradients with ne…
Federated constrained learning allows us to learn a global model with some specific structure to enhance performance. Most existing federated learning techniques assume data is homogeneously or independently and identically distributed (iid) across clients. However, this iid assumption rarely holds…
In this paper, we propose a non-parametric task-agnostic representation learning method for cell complexes (CCs). Specifically, we propose a scattering transform for CCs that extends geometric scattering to CCs. In addition, we introduce scattering transforms for product cell complexes (PCCs), which…
In this paper, we propose DP-FedFW, a novel Frank-Wolfe based federated learning algorithm with local (ϵ,δ)-differential privacy (DP) guarantees in a constrained learning setting. In DP-FedFW, we perturb local models to ensure privacy while communicating with the server, and each client performs sev…
Simplicial neural networks (SNNs) are deep models for higher-order graph representation learning. SNNs learn low-dimensional embeddings of simplices in a simplicial complex by aggregating features of their respective upper, lower, boundary, and coboundary adjacent simplices. The aggregation in SNNs…
We consider recovery of signals over product cell structures from subsampled observations. In particular, we consider product cell complexes, which can be factorized as the Cartesian product of two simplicial complexes. We focus on recovering edge and node signals from subsampled signals on the fact…
Simplicial neural network models are becoming popular for processing and analyzing higher-order graph data, but they suffer from high training complexity and dependence on task-specific labels. To address these challenges, we propose simplicial scattering networks (SSNs), a parameter-free model insp…
In this paper, we present a novel reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with 1-bit quantization at the ISAC base station. An RIS is introduced in the ISAC system to mitigate the effects of coarse quantization and to enable the co-existen…
In this paper, we introduce $\texttt{TopoSRL}$, a novel self-supervised learning (SSL) method for simplicial complexes to effectively capture higher-order interactions and preserve topology in the learned representations. $\texttt{TopoSRL}$ addresses the limitations of existing graph-based SSL metho…
In this paper, we focus on learning sparse graphs with a core-periphery structure. We propose a generative model for data associated with core-periphery structured networks to model the dependence of node attributes on core scores of the nodes of a graph through a latent graph structure. Using the p…
This paper focuses on channel estimation for mmWave MIMO systems with 1-bit spatial sigma-delta analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). The channel estimation performance with 1-bit spatial sigma-delta modulators (i.e., ADCs or DACs) depends on the quantization n…
We present a novel multiview canonical correlation analysis model based on a variational approach. This is the first nonlinear model that takes into account the available graph-based geometric constraints while being scalable for processing large scale datasets with multiple views. It is based on an…
We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a distribution of signals that are smooth or bandlimited on a known graph. However, instead of observing these signals, we…
In this paper, we focus on learning the underlying product graph structure from multidomain training data. We assume that the product graph is formed from a Cartesian graph product of two smaller factor graphs. We then pose the product graph learning problem as the factor graph Laplacian matrix esti…
In this paper, the focus is on the gain and phase calibration of sparse sensor arrays to localize more sources than the number of physical sensors. The proposed technique is a blind calibration method as it does not require any calibrator sources. Joint estimation of the gain errors, phase errors, a…
In this paper, we present a calibration algorithm for acoustic vector sensors arranged in a uniform linear array configuration. To do so, we do not use a calibrator source, instead we leverage the Toeplitz blocks present in the data covariance matrix. We develop linear estimators for estimating sens…
An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compu…
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary graphs or arbitrary signals that may be represented using graphs, where we extend concepts from generalized sampling theory to the graph setting. To recover such signals from a given set of samples,…
In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean…
We study the sensor selection problem for field estimation, where a best subset of sensors is activated to monitor a spatially correlated random field. Different from most commonly used centralized selection algorithms, we propose a decentralized architecture where sensor selection can be carried ou…
In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the graph topology, and we p…
In this paper we focus on the relative position and orientation estimation between rigid bodies in an anchorless scenario. Several sensor units are installed on the rigid platforms, and the sensor placement on the rigid bodies is known beforehand (i.e., relative locations of the sensors on the rigid…
An offline sampling design problem for Gaussian detection is considered in this paper. The sensing operation ismodeled by a selection vector, whose sparsity order is determined by the prescribed global error probability. Since the numerical optimization of the error probability is difficult, equival…