arXiv research

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

The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial sparse group lasso classifi…

Sparse multinomial logistic regression for multiclass classification with feature selection.

PIANO speeds up multinomial logistic regression solving.

FJS method improves multinomial classification accuracy.

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

We improve MoE models for classification with rigorous guarantees and practical methods.

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…

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…

We improve random forest consistency and performance with DMRF, a new variant.

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 conjugate priors improve Bayesian inference for multinomial probit models.

SJS model predicts label shifts in multinomial datasets.

Enhances random forest consistency and introduces DMRF for improved performance.

The paper establishes convergence rates for MoE models in classification problems.

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

In latent Dirichlet allocation (LDA), topics are multinomial distributions over the entire vocabulary. However, the vocabulary usually contains many words that are not relevant in forming the topics. We adopt a variable selection method widely used in statistical modeling as a dimension reduction tool and combine it wi…

We define a new method to estimate centroid for text classification based on the symmetric KL-divergence between the distribution of words in training documents and their class centroids. Experiments on several standard data sets indicate that the new method achieves substantial improvements over the traditional classi…

Trans-GCR uses GCR model for node classification, providing theoretical guarantees and superior performance.

We present ADMM-Softmax, an alternating direction method of multipliers (ADMM) for solving multinomial logistic regression (MLR) problems. Our method is geared toward supervised classification tasks with many examples and features. It decouples the nonlinear optimization problem in MLR into three steps that can be solv…

Optimal projections enhance Naive Bayes classification.

Many text classification tasks are known to be highly domain-dependent. Unfortunately, the availability of training data can vary drastically across domains. Worse still, for some domains there may not be any annotated data at all. In this work, we propose a multinomial adversarial network (MAN) to tackle the text clas…

Log-concavity proven for multinomial likelihoods under specific constraints.

Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging. This is primarily because one needs to compute the log-partition function on every data point. This makes distributing the computation hard. In this paper, we present a distributed stochastic gradient d…

Proposes a method to use external machine-learning predictions in multinomial logistic regression.

For the problem of multi-class linear classification and feature selection, we propose approximate message passing approaches to sparse multinomial logistic regression (MLR). First, we propose two algorithms based on the Hybrid Generalized Approximate Message Passing (HyGAMP) framework: one finds the maximum a posterio…

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…

Develops a functional mix-of-experts model for multiclass classification.

This work embeds annotations into a multidimensional space to measure classification difficulty.

New method tests risk measures for various distortions.

We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relative volume of a given set of points. We derive the details for a family of metrics on the multinomial simplex. The resulting metric has appl…

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…

Approximates large Random Forest models to save space.

Machine Learning has become very famous currently which assist in identifying the patterns from the raw data. Technological advancement has led to substantial improvement in Machine Learning which, thus helping to improve prediction. Current Machine Learning models are based on Classical Theory, which can be replaced b…

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

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

New algorithm reduces switching costs in multinomial logit bandit problems.

New Gamma-Poisson model improves topic selection for short text.

Deviance-style normalization for sparse, jointly overdispersed count matrices

Paper introduces new methods for modeling categorical data.

We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an…

Efficient RL algorithm for multinomial logistic MDPs with provable guarantees.

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

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

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…

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…

New method calculates DMN log-likelihood faster.

The paper develops approximations for Pearson's chi-square statistic and applies them to confidence intervals.