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.
Efficient EP algorithm improves smoothing distribution inference in financial models.
New method for efficient maximum likelihood estimation of -generalized probit regression.
The logistic normal distribution has recently been adapted via the transformation of multivariate Gaus- sian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. O…
Probit Monotone BART estimates binary outcomes using monotonic functions.
This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. T…
EP method speeds up Bayesian probit regression in high dimensions.
New conjugate priors improve Bayesian inference for multinomial probit models.
Binary classification models get more efficient predictive probabilities.
Bayesian methods improve inference for cumulative probit models on large datasets.
MPVAE learns latent embeddings and label correlations for multi-label classification.
We study the dynamical behavior of high-frequency data from the Korean Stock Price Index (KOSPI) using the movement of returns in Korean financial markets. The dynamical behavior for a binarized series of our models is not completely random. The conditional probability is numerically estimated from a return series of K…
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multidimensional constrained space of latent variables, significantly limits its a…
The study examines mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression.
Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain t…
Bayesian method models binary response and covariates for two groups, estimating causal relationships.
Proposes a new tensor factorization model for better link prediction in knowledge graphs.
An efficient algorithm selects the correct number of latent dimensions in multidimensional probit models.
Paper develops Bayesian inference for discrete-choice mnp models with Gaussian priors.
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure…
Efficiently identifies important variables in binary outcomes using variational Bayes.
Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields. This paper provides a fresh angle to such well-established…
We consider probabilistic multinomial probit classification using Gaussian process (GP) priors. The challenges with the multiclass GP classification are the integration over the non-Gaussian posterior distribution, and the increase of the number of unknown latent variables as the number of target classes grows. Expecta…
Novel Bayesian model improves EEG-based BCI character selection.
Improves graph-based active learning for non-Gaussian models.
SBPMT combines bagging and boosting for improved classification.
Spectral method speeds fitting of binary time series models.
Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data. This paper is concerned with the consistency of optimization-based techniques for such problems, in the limit where the labels have small noise and the underlying unl…
We study convex empirical risk minimization for high-dimensional inference in binary models. Our first result sharply predicts the statistical performance of such estimators in the linear asymptotic regime under isotropic Gaussian features. Importantly, the predictions hold for a wide class of convex loss functions, wh…
This paper provides a theoretical and computational justification of the long held claim that of the similarity of the probit and logit link functions often used in binary classification. Despite this widespread recognition of the strong similarities between these two link functions, very few (if any) researchers have …
We analyze mixing times of three DA algorithms for regression models.
New algorithm learns sparse GLMs for binary outcomes efficiently.
Unified Skew-Gaussian process framework for various regression and classification tasks.
New algorithms improve MPBART for HIV patient data.
Characterizes uncertainty in high-dimensional linear classification models.
Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be as important as the classification itself. In this paper we introduce, develop algorithms for, and investigate the properties of, a variety o…
Paper introduces symmetric divergence link models for probability distributions.
Two methods use BART to model missing data in leaf photosynthetic trait data.
Deep model tackles zero-inflated multi-species abundance estimation.
Develops a flexible model for regime transitions in time series data.
The logistic regression model is known to converge to a Poisson point process model if the binary response tends to infinitely imbalanced. In this paper, it is shown that this phenomenon is universal in a wide class of link functions on binomial regression. The proof relies on the extreme value theory. For the logit, p…
Algorithm improves variational inference in Wasserstein distance.
With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical samples, the typically streaming format of acquisit…
We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent comp…
Black-box alpha (BB-) is a new approximate inference method based on the minimization of -divergences. BB- scales to large datasets because it can be implemented using stochastic gradient descent. BB- can be applied to complex probabilistic models with little effort since it only requires as input the likel…
Designs efficient algorithms for online and sliding window models of subspace embeddings for all p.
Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. However, when the number of possible outcomes is very large, using categorical distributions becomes computationally expensive, as the complexity scales linearly with the number of outcome…
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…
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…