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Category: eess.SP

  • Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models

    Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models arXiv:2602.15920v1 Announce Type: new Abstract: This paper addresses graph learning in Gaussian Graphical Models (GGMs). In this context, data matrices often come with auxiliary metadata (e.g., textual descriptions associated with each node) that is usually ignored in traditional graph estimation processes. To fill this gap, we…

  • Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem

    Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem arXiv:2601.06009v1 Announce Type: new Abstract: We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our approach is based on classical excursion and crossing theorems for continuous semimartingales, which correlates number $N_varepsilon$ of excursions of magnitude…

  • Colored Markov Random Fields for Probabilistic Topological Modeling

    Colored Markov Random Fields for Probabilistic Topological Modeling arXiv:2512.03727v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint distribution into lower-dimensional components. This makes PGMs well-suited for analyzing complex systems and supporting decision-making. Recent advances in topological…

  • A new class of Markov random fields enabling lightweight sampling

    A new class of Markov random fields enabling lightweight sampling arXiv:2511.02373v1 Announce Type: new Abstract: This work addresses the problem of efficient sampling of Markov random fields (MRF). The sampling of Potts or Ising MRF is most often based on Gibbs sampling, and is thus computationally expensive. We consider in this work how to circumvent…

  • A Short Note on Upper Bounds for Graph Neural Operator Convergence Rate

    A Short Note on Upper Bounds for Graph Neural Operator Convergence Rate arXiv:2510.20954v1 Announce Type: new Abstract: Graphons, as limits of graph sequences, provide a framework for analyzing the asymptotic behavior of graph neural operators. Spectral convergence of sampled graphs to graphons yields operator-level convergence rates, enabling transferability analyses of GNNs. This note summarizes known…

  • Simplicial Gaussian Models: Representation and Inference

    Simplicial Gaussian Models: Representation and Inference arXiv:2510.12983v1 Announce Type: new Abstract: Probabilistic graphical models (PGMs) are powerful tools for representing statistical dependencies through graphs in high-dimensional systems. However, they are limited to pairwise interactions. In this work, we propose the simplicial Gaussian model (SGM), which extends Gaussian PGM to simplicial complexes. SGM jointly models random…

  • On the Adversarial Robustness of Learning-based Conformal Novelty Detection

    On the Adversarial Robustness of Learning-based Conformal Novelty Detection arXiv:2510.00463v1 Announce Type: new Abstract: This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on AdaDetect, a powerful learning-based framework for novelty detection with finite-sample false discovery rate (FDR) control. While AdaDetect provides rigorous statistical guarantees under benign conditions, its behavior…

  • General Pruning Criteria for Fast SBL

    General Pruning Criteria for Fast SBL arXiv:2509.21572v1 Announce Type: new Abstract: Sparse Bayesian learning (SBL) associates to each weight in the underlying linear model a hyperparameter by assuming that each weight is Gaussian distributed with zero mean and precision (inverse variance) equal to its associated hyperparameter. The method estimates the hyperparameters by marginalizing out the…

  • Maximum diversity, weighting and invariants of time series

    Maximum diversity, weighting and invariants of time series arXiv:2509.11146v1 Announce Type: new Abstract: Magnitude, obtained as a special case of Euler characteristic of enriched category, represents a sense of the size of metric spaces and is related to classical notions such as cardinality, dimension, and volume. While the studies have explained the meaning of magnitude…

  • Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data

    Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data arXiv:2508.11693v1 Announce Type: cross Abstract: Track Circuits (TC) are the main signalling devices used to detect the presence of a train on a rail track. It has been used since the 19th century and nowadays there are many types depending on the…

  • Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation

    Adaptive Iterative Soft-Thresholding Algorithm with the Median Absolute Deviation arXiv:2507.02084v1 Announce Type: new Abstract: The adaptive Iterative Soft-Thresholding Algorithm (ISTA) has been a popular algorithm for finding a desirable solution to the LASSO problem without explicitly tuning the regularization parameter $lambda$. Despite that the adaptive ISTA is a successful practical algorithm, few theoretical results exist.…

  • Derandomizing Simultaneous Confidence Regions for Band-Limited Functions by Improved Norm Bounds and Majority-Voting Schemes

    Derandomizing Simultaneous Confidence Regions for Band-Limited Functions by Improved Norm Bounds and Majority-Voting Schemes arXiv:2506.17764v1 Announce Type: new Abstract: Band-limited functions are fundamental objects that are widely used in systems theory and signal processing. In this paper we refine a recent nonparametric, nonasymptotic method for constructing simultaneous confidence regions for band-limited functions from noisy input-output…

  • Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties

    Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties arXiv:2505.11984v1 Announce Type: new Abstract: We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models,…

  • Learning Multi-Attribute Differential Graphs with Non-Convex Penalties

    Learning Multi-Attribute Differential Graphs with Non-Convex Penalties arXiv:2505.09748v1 Announce Type: new Abstract: We consider the problem of estimating differences in two multi-attribute Gaussian graphical models (GGMs) which are known to have similar structure, using a penalized D-trace loss function with non-convex penalties. The GGM structure is encoded in its precision (inverse covariance) matrix. Existing methods…

  • Towards Accurate Forecasting of Renewable Energy : Building Datasets and Benchmarking Machine Learning Models for Solar and Wind Power in France

    Towards Accurate Forecasting of Renewable Energy : Building Datasets and Benchmarking Machine Learning Models for Solar and Wind Power in France arXiv:2504.16100v1 Announce Type: cross Abstract: Accurate prediction of non-dispatchable renewable energy sources is essential for grid stability and price prediction. Regional power supply forecasts are usually indirect through a bottom-up approach of plant-level forecasts,…

  • Matched Topological Subspace Detector

    Matched Topological Subspace Detector arXiv:2504.05892v1 Announce Type: new Abstract: Topological spaces, represented by simplicial complexes, capture richer relationships than graphs by modeling interactions not only between nodes but also among higher-order entities, such as edges or triangles. This motivates the representation of information defined in irregular domains as topological signals. By leveraging the spectral dualities…

  • Denoising guarantees for optimized sampling schemes in compressed sensing

    Denoising guarantees for optimized sampling schemes in compressed sensing arXiv:2504.01046v1 Announce Type: new Abstract: Compressed sensing with subsampled unitary matrices benefits from emph{optimized} sampling schemes, which feature improved theoretical guarantees and empirical performance relative to uniform subsampling. We provide, in a first of its kind in compressed sensing, theoretical guarantees showing that the error caused…

  • Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging

    Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging arXiv:2501.13109v1 Announce Type: cross Abstract: Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly. A physics-based forward model that relates the signal with the observations is typically needed. Unfortunately, unknown model…

  • Asymptotically Optimal Search for a Change Point Anomaly under a Composite Hypothesis Model

    Asymptotically Optimal Search for a Change Point Anomaly under a Composite Hypothesis Model arXiv:2412.19392v1 Announce Type: new Abstract: We address the problem of searching for a change point in an anomalous process among a finite set of M processes. Specifically, we address a composite hypothesis model in which each process generates measurements following a common…

  • Fast Multi-Group Gaussian Process Factor Models

    Fast Multi-Group Gaussian Process Factor Models arXiv:2412.16773v1 Announce Type: new Abstract: Gaussian processes are now commonly used in dimensionality reduction approaches tailored to neuroscience, especially to describe changes in high-dimensional neural activity over time. As recording capabilities expand to include neuronal populations across multiple brain areas, cortical layers, and cell types, interest in extending Gaussian…

  • Deep Learning for Hydroelectric Optimization: Generating Long-Term River Discharge Scenarios with Ensemble Forecasts from Global Circulation Models

    Deep Learning for Hydroelectric Optimization: Generating Long-Term River Discharge Scenarios with Ensemble Forecasts from Global Circulation Models arXiv:2412.12234v1 Announce Type: cross Abstract: Hydroelectric power generation is a critical component of the global energy matrix, particularly in countries like Brazil, where it represents the majority of the energy supply. However, its strong dependence on river discharges,…

  • Learning Networks from Wide-Sense Stationary Stochastic Processes

    Learning Networks from Wide-Sense Stationary Stochastic Processes arXiv:2412.03768v1 Announce Type: new Abstract: Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from node outputs (potentials). We focus on systems governed by steady-state linear conservation laws: $X_t = {L^{ast}}Y_{t}$,…

  • Nonlinearity and Uncertainty Informed Moment-Matching Gaussian Mixture Splitting

    Nonlinearity and Uncertainty Informed Moment-Matching Gaussian Mixture Splitting arXiv:2412.00343v1 Announce Type: new Abstract: Many problems in navigation and tracking require increasingly accurate characterizations of the evolution of uncertainty in nonlinear systems. Nonlinear uncertainty propagation approaches based on Gaussian mixture density approximations offer distinct advantages over sampling based methods in their computational cost and continuous representation.…