A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method for hyperparameter tuning in sparse matrix factorization.
New approach uses unlabeled prior data to accelerate exploration in sparse reward tasks.
The paper analyzes uncertainty quantification in sparse Gaussian process regression with a Brownian motion prior.
The paper extends and applies a new shrinkage prior in Bayesian factor analysis.
Proposes a tail-adaptive shrinkage method for robust sparse estimation.
New theory for BNNs with Gaussian priors achieves optimal posterior concentration rates.
Study compares L1 and VG sparsity priors in inverse problems.
New method uses generative priors for compressive sensing with sparse solutions.
Pixel-wise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results …
VolNP learns IVS from sparse quotes via meta-learning and SABR priors.
RG-Flow combines RG and sparse priors for hierarchical image disentanglement.
The paper improves Bayesian precision matrix estimation for high-dimensional sparse data.
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse Bayesian learning method is developed for robust compressed sensing. The basic idea of the proposed method is to identify and remove the ou…
IDS improves sparse linear bandits by balancing information and regret.
Proposes EM for sparse horseshoe estimation.
Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the l1-norm of the parameter of interest. However, other penalization terms have proven to have…
Proposes a Bayesian approach for automatic node selection in sparse neural networks.
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation propagation (EP) approach for approximate integration over the posterior distribution of the weights, the hierarchical s…
In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, a…
Flexible Bayesian approach for generalized linear models, especially for sparse logistic regression.
This paper solves quadratic systems with sparse or generative priors.
Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted norm is usually employed for the regularization term in sparse convex clustering, its use increases the dependence on the data and reduces the estimation acc…
Genome-wide association studies (GWA studies or GWAS) investigate the relationships between genetic variants such as single-nucleotide polymorphisms (SNPs) and individual traits. Recently, incorporating biological priors together with machine learning methods in GWA studies has attracted increasing attention. However, …
So-called sparse estimators arise in the context of model fitting, when one a priori assumes that only a few (unknown) model parameters deviate from zero. Sparsity constraints can be useful when the estimation problem is under-determined, i.e. when number of model parameters is much higher than the number of data point…
MAXENT method outperforms ML in sparse data with specific prior correlations.
Paper develops efficient variational inference for sparse deep learning with theoretical guarantees.
New sparse GP model learns compositional kernels efficiently.
New FGSPCA method captures grouping and sparse structures in PCA without prior info.
spex-LVM infers interpretable latent factors from biomedical data.
Proposes a Bayesian Autoencoder with sparse Gaussian process priors to capture data correlations.
Bayesian pliable lasso with horseshoe prior models interactions in GLMs with missing data.
In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases inclu…
New framework tackles deep learning issues like local traps and miscalibration.
CtrlNS learns latent factors and distribution shifts from sparse transitions without prior knowledge.
The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.
Improved Thompson Sampling for high-dimensional sparse bandits.
In many problem settings, parameter vectors are not merely sparse but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependency as "region sparsity." Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), which model par…
Improved VB algorithm for high-dimensional logistic regression with theoretical guarantees.
In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly …
We consider the problem of recovering block-sparse signals whose structures are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. However, the knowledge of the block structure is usually unavailable in practice. In this paper, we d…
Sparse transformer architecture improves accuracy and speed in generative modeling and inverse problems.
We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP) priors that allow nonzero covariance between grouped latent functions. We show that when these grouped functions are conditionally independ…
ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary components that have generated the data. The library widens the scope of dictionary learning approaches beyond implementations of standard approaches…
The fused lasso penalizes a loss function by the norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed in…
A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce the number of projection views (sparse-view CT); however, it becomes difficult to achieve high-quality image reconstruction as the number of…
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian p…
Paper proposes new Bayesian neural network models for efficient learning.
In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal. Existing approaches leverage assumptions on the structure of these signals, such as sparsity or the availability of a generative model. A domain-specific generative model can provide a stronger prior and thus allo…