arXiv research

New conjugate priors improve Bayesian inference for multinomial probit models.

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…

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…

Paper develops Bayesian inference for discrete-choice mnp models with Gaussian priors.

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…

New algorithms improve MPBART for HIV patient data.

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…

New method for efficient maximum likelihood estimation of $p$-generalized probit regression.

Binary classification models get more efficient predictive probabilities.

Probit Monotone BART estimates binary outcomes using monotonic functions.

Efficient EP algorithm improves smoothing distribution inference in financial models.

Bayesian methods improve inference for cumulative probit models on large datasets.

EP method speeds up Bayesian probit regression in high dimensions.

MPVAE learns latent embeddings and label correlations for multi-label classification.

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…

Proposes a new tensor factorization model for better link prediction in knowledge graphs.

Bayesian method models binary response and covariates for two groups, estimating causal relationships.

An efficient algorithm selects the correct number of latent dimensions in multidimensional probit models.

Efficiently models categorical data with low to medium class overlap, improving accuracy over standard distributions.

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…

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…

Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space. Conventional GPCs however suffer from (i) poor scalability for big data due to the full kernel matrix, and (ii) intractable inference due to the non-Gaussian likelihoods. Henc…

The study examines mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression.

Improves graph-based active learning for non-Gaussian models.

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…

Novel Bayesian model improves EEG-based BCI character selection.

PIANO speeds up multinomial logistic regression solving.

SBPMT combines bagging and boosting for improved classification.

To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binar…

Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions. In all of these cas…

Efficiently identifies important variables in binary outcomes using variational Bayes.

New method tests risk measures for various distortions.

Paper introduces new methods for modeling categorical data.

Sparse multinomial logistic regression for multiclass classification with feature selection.

Proposes a new model for context-dependent decision-making.

Beam search improves UQ in LLMs by reducing duplicates and variance.

Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis. Generalized linear models enable direct modeling of counts in a regression context using distributions such as the Poisson and negati…

Efficient RL algorithm for multinomial logistic MDPs with provable guarantees.

Log-concavity proven for multinomial likelihoods under specific constraints.

We consider a problem of clustering a sequence of multinomial observations by way of a model selection criterion. We propose a form of a penalty term for the model selection procedure. Our approach subsumes both the conventional AIC and BIC criteria but also extends the conventional criteria in a way that it can be app…

The paper proposes an efficient method to scale Bayesian inference for mixed multinomial logit models to very large datasets.

Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.

Paper introduces a new text clustering model using Beta-Liouville priors.

Sharp bounds on binary model inference performance.

New method for epidemic model inference using multinomial approximations.

The paper proves asymptotic normality for multinomial logistic regression on null covariates.

Deviance-style normalization for sparse, jointly overdispersed count matrices

We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression. This convex relaxation of reduced-rank multinomial regression has the advantage of leveraging underlying structure among the response categories to make better predictions. We apply our method, nuclear pen…