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Tag: sparse

  • Combinatorial Sparse PCA Beyond the Spiked Identity Model

    Combinatorial Sparse PCA Beyond the Spiked Identity Model arXiv:2603.02607v1 Announce Type: new Abstract: Sparse PCA is one of the most well-studied problems in high-dimensional statistics. In this problem, we are given samples from a distribution with covariance $Sigma$, whose top eigenvector $v in R^d$ is $s$-sparse. Existing sparse PCA algorithms can be broadly categorized into…

  • Routing in a Sparse Graph: a Distributed Q-Learning Approach

    Routing in a Sparse Graph: a Distributed Q-Learning Approach Distributed agents need only decide one move ahead. The post Routing in a Sparse Graph: a Distributed Q-Learning Approach appeared first on Towards Data Science. Sébastien Gilbert Go to original source

  • Physics-informed Blind Reconstruction of Dense Fields from Sparse Measurements using Neural Networks with a Differentiable Simulator

    Physics-informed Blind Reconstruction of Dense Fields from Sparse Measurements using Neural Networks with a Differentiable Simulator arXiv:2601.20496v1 Announce Type: new Abstract: Generating dense physical fields from sparse measurements is a fundamental question in sampling, signal processing, and many other applications. State-of-the-art methods either use spatial statistics or rely on examples of dense fields in the…

  • Perfect Clustering for Sparse Directed Stochastic Block Models

    Perfect Clustering for Sparse Directed Stochastic Block Models arXiv:2601.16427v1 Announce Type: new Abstract: Exact recovery in stochastic block models (SBMs) is well understood in undirected settings, but remains considerably less developed for directed and sparse networks, particularly when the number of communities diverges. Spectral methods for directed SBMs often lack stability in asymmetric, low-degree regimes,…

  • Learning from Neighbors with PHIBP: Predicting Infectious Disease Dynamics in Data-Sparse Environments

    Learning from Neighbors with PHIBP: Predicting Infectious Disease Dynamics in Data-Sparse Environments arXiv:2512.21005v1 Announce Type: new Abstract: Modeling sparse count data, which arise across numerous scientific fields, presents significant statistical challenges. This chapter addresses these challenges in the context of infectious disease prediction, with a focus on predicting outbreaks in geographic regions that have historically…

  • Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them.

    Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. Neural and symbolic models compress the world in fundamentally different ways, and Sparse Autoencoders (SAEs) offer a bridge to connect them. The post Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. appeared first on Towards…

  • PCA recovery thresholds in low-rank matrix inference with sparse noise

    PCA recovery thresholds in low-rank matrix inference with sparse noise arXiv:2511.11927v1 Announce Type: new Abstract: We study the high-dimensional inference of a rank-one signal corrupted by sparse noise. The noise is modelled as the adjacency matrix of a weighted undirected graph with finite average connectivity in the large size limit. Using the replica method from…

  • Graphical model for tensor factorization by sparse sampling

    Graphical model for tensor factorization by sparse sampling arXiv:2510.17886v1 Announce Type: new Abstract: We consider tensor factorizations based on sparse measurements of the tensor components. The measurements are designed in a way that the underlying graph of interactions is a random graph. The setup will be useful in cases where a substantial amount of data…

  • Nonparametric learning of stochastic differential equations from sparse and noisy data

    Nonparametric learning of stochastic differential equations from sparse and noisy data arXiv:2508.11597v1 Announce Type: new Abstract: The paper proposes a systematic framework for building data-driven stochastic differential equation (SDE) models from sparse, noisy observations. Unlike traditional parametric approaches, which assume a known functional form for the drift, our goal here is to learn the entire…

  • Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures

    Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures arXiv:2507.19787v1 Announce Type: new Abstract: The dynamic mode decomposition (DMD) is a data-driven approach that extracts the dominant features from spatiotemporal data. In this work, we introduce sparse-mode DMD, a new variant of the optimized DMD framework that specifically leverages sparsity-promoting regularization in order to…

  • Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited

    Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited arXiv:2507.02377v1 Announce Type: new Abstract: Inducing-point-based sparse variational Gaussian processes have become the standard workhorse for scaling up GP models. Recent advances show that these methods can be improved by introducing a diagonal scaling matrix to the conditional posterior density given the inducing points. This paper first…

  • A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection

    A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection arXiv:2505.10099v1 Announce Type: new Abstract: Portfolio optimization involves selecting asset weights to minimize a risk-reward objective, such as the portfolio variance in the classical minimum-variance framework. Sparse portfolio selection extends this by imposing a cardinality constraint: only $k$ assets from a universe of $p$ may…

  • Sparse Gaussian Neural Processes

    Sparse Gaussian Neural Processes arXiv:2504.01650v1 Announce Type: new Abstract: Despite significant recent advances in probabilistic meta-learning, it is common for practitioners to avoid using deep learning models due to a comparative lack of interpretability. Instead, many practitioners simply use non-meta-models such as Gaussian processes with interpretable priors, and conduct the tedious procedure of training their…

  • Sparse AutoEncoder: from Superposition to interpretable features

    Sparse AutoEncoder: from Superposition to interpretable features Disentangle features in complex Neural Network with superpositions Complex neural networks, such as Large Language Models (LLMs), suffer quite often from interpretability challenges. One of the most important reasons for such difficulty is superposition — a phenomenon of the neural network having fewer dimensions than the number of features it…

  • Nonparametric Sparse Online Learning of the Koopman Operator

    Nonparametric Sparse Online Learning of the Koopman Operator arXiv:2501.16489v1 Announce Type: new Abstract: The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman…

  • Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks

    Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks arXiv:2501.05930v1 Announce Type: cross Abstract: We present a framework to define a large class of neural networks for which, by construction, training by gradient flow provably reaches arbitrarily low loss when the number of parameters grows. Distinct from the fixed-space global optimality of non-convex…