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.
The paper improves Bayesian precision matrix estimation for high-dimensional sparse data.
Develops a new multivariate regression model for complex outcomes.
Proposes a new method to control FDR using frequentist-assisted horseshoe for high-dimensional testing.
The paper proposes a semi-parametric Bayesian network model using Gaussian Processes and Horseshoe priors.
T-LoHo model detects structured sparsity and smoothness on graph data.
We propose a new Bayesian model for flexible nonlinear regression and classification using tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu (2008) where rules from decision trees and linear terms are used in a L1-regularized regression. We modify RuleFit by replacing the L1-regularizat…
Bayesian method for estimating functional graphical models from neuroimaging data.
Proposes EM for sparse horseshoe estimation.
Bayesian method improves sparse CCA for multi-view data.
Bayesian tree ensemble model for estimating treatment effects in high-dimensional survival data.
Horseshoe priors improve small area estimation by borrowing strength globally but locally.
Bayesian pliable lasso with horseshoe prior models interactions in GLMs with missing data.
HS-BQR extends horseshoe prior for Bayesian quantile regression.
Bayesian Tobit model tackles high-dimensional censored data with Horseshoe prior.
HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.
Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation, …
Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties. However, model selection---even choosing the number of nodes---remains an open question. Recent work has proposed the use of a horseshoe prior over node pre-activations of a …
Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties. However, model selection---even choosing the number of nodes---remains an open question. In this work, we apply a horseshoe prior over node pre-activations of a Bayesian neur…
In this paper, the use of the Generalized Beta Mixture (GBM) and Horseshoe distributions as priors in the Bayesian Compressive Sensing framework is proposed. The distributions are considered in a two-layer hierarchical model, making the corresponding inference problem amenable to Expectation Maximization (EM). We prese…
Bayesian Beta regression for proportions in high dimensions with theoretical guarantees.
Proposes a tail-adaptive shrinkage method for robust sparse estimation.
Paper proposes new Bayesian neural network models for efficient learning.
Deep-HGP uses Bayesian nonparametric approach for complex data regression.
Proposes a method to identify elements in a skewness matrix for multivariate skew-elliptical distributions.
Item response theory (IRT) is a non-linear generative probabilistic paradigm for using exams to identify, quantify, and compare latent traits of individuals, relative to their peers, within a population of interest. In pre-existing multidimensional IRT methods, one requires a factorization of the test items. For this t…
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics. The penalty is ideal for this task, the caveat being it requires the NP-hard combinatorial evaluation of all models. A recent area of considerable interest is to develop efficient algor…
We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are k…
GRASP simplifies Bayesian regression with grouped predictors using an adaptive NBP prior.
Global results are proved about the way in which Boyland's forcing partial order organizes a set of braid types: those of periodic orbits of Smale's horseshoe map for which the associated train track is a star. This is a special case of a conjecture introduced in a previous paper, which claims that forcing organizes al…
Let T be the nilpotent group of 4 x 4 real upper triangular matrices. In this note we show that the Euler equations of certain left-invariant riemannian metrics on T have a horseshoe. We also show, with the aid of a numerical computation of a Melnikov-type integral, that the Euler equations of the sub-riemannian Carnot…
Algorithm estimates graph structure with prior information and Langevin diffusion.
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally…
Bayesian inference corrected for bias in high-dimensional models.
New sparse GP model learns compositional kernels efficiently.
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary graph structure without invoking any assumptions about decomposability. The majority…
Develops methods for constructing parameter priors in DAG models.
Generating user interpretable multi-class predictions in data rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised topic model for multi-class classification that can handle both many classes as well as many co…
rags2ridges simplifies graphical modeling of high-dimensional data.
In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce fully Bayesian treatments of two popular procedures, the group graphical lasso and the fused graphical lasso, and extend them to a continuous spike-and-slab framework to allow self-adaptive shr…
GmGM models multi-axis data for faster analysis.
We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family distributions, but i…
Bayesian networks combine prior knowledge with data to learn causal relationships.
Graphical model learning and inference are often performed using Bayesian techniques. In particular, learning is usually performed in two separate steps. First, the graph structure is learned from the data; then the parameters of the model are estimated conditional on that graph structure. While the probability distrib…
We present a framework for incorporating prior information into nonparametric estimation of graphical models. To avoid distributional assumptions, we restrict the graph to be a forest and build on the work of forest density estimation (FDE). We reformulate the FDE approach from a Bayesian perspective, and introduce pri…
Gaussian graphical models are relevant tools to learn conditional independence structure between variables. In this class of models, Bayesian structure learning is often done by search algorithms over the graph space. The conjugate prior for the precision matrix satisfying graphical constraints is the well-known G-Wish…
The paper defines and analyzes homotopic rotation sets for surfaces of higher genus.
This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical model selection algorithms are passive, i.e., they require all the measurements to have been collected before processing begins. We propose an…
Bayesian method learns network structure from Gaussian process priors.