arXiv research

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

Log-concavity proven for multinomial likelihoods under specific constraints.

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

Deviance-style normalization for sparse, jointly overdispersed count matrices

Private two-sample tests under LDP achieve minimax rates for multinomial and continuous data.

Improves change-point detection for high-dimensional time-series.

Deep generative model for healthcare data identifies coherent substructures and mutational clusters.

FJS method improves multinomial classification accuracy.

The paper tackles learning mixtures of two multinomial logits, showing identifiability and presenting an algorithm.

Develops a measure for checking cluster quality using multinomial distribution.

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

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of conver…

New conjugate priors improve Bayesian inference for multinomial probit models.

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

The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel. To address this non-conjugacy concern, we propose the application of Pólygamma data a…

Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements. The reason for this is that samples from a distribution on discrete objects such as the multinomial are not differentiable with respect to the distribution parameters. This problem can be avoided by using…

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

PIANO speeds up multinomial logistic regression solving.

Under the Fundamental Review of the Trading Book (FRTB) capital charges for the trading book are based on the coherent expected shortfall (ES) risk measure, which show greater sensitivity to tail risk. In this paper it is argued that backtesting of expected shortfall - or the trading book model from which it is calcula…

This paper studies the geometry of minimum-volume confidence sets for multinomial parameters.

Optimal design for multinomial logit models improves assortment selection efficiency.

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…

PQMass assesses generative model quality using chi-squared tests.

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…

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

New method tests risk measures for various distortions.

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…

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…

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…

Sparse multinomial logistic regression for multiclass classification with feature selection.

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

New algorithm reduces switching costs in multinomial logit bandit problems.

Identifies the best-performing algorithm from a set of candidates.

Study optimizes product assortment for retailers with repeated exposures and patience costs.

Paper introduces new methods for modeling categorical data.

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…

Improved logistic MoE with sigmoid gate shows better sample efficiency.

Study optimizes dynamic product selection and pricing using censored preference feedback.

Enhances random forest consistency and introduces DMRF for improved performance.

Efficient RL algorithm for multinomial logistic MDPs with provable guarantees.

Construction of tight confidence regions and intervals is central to statistical inference and decision making. This paper develops new theory showing minimum average volume confidence regions for categorical data. More precisely, consider an empirical distribution $\widehat{\boldsymbol{p}}$ generated from $n$ iid real…

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.

Proposes a new model for clustering passenger trajectories with graphs.

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