arXiv research

BEGIN network models binary data without parametric assumptions.

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

TabSODA improves imputation of surveys with skips and ordinal data.

Log-concavity proven for multinomial likelihoods under specific constraints.

Multinomial Logistic Regression is a well-studied tool for classification and has been widely used in fields like image processing, computer vision and, bioinformatics, to name a few. Under a supervised classification scenario, a Multinomial Logistic Regression model learns a weight vector to differentiate between any …

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…

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…

New method tests risk measures for various distortions.

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.

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.

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

Deviance-style normalization for sparse, jointly overdispersed count matrices

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…

Enhances random forest consistency and introduces DMRF for improved performance.

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.

New conjugate priors improve Bayesian inference for multinomial probit models.

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.

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

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

Sigma-Pi-Sigma neural networks (SPSNNs) as a kind of high-order neural networks can provide more powerful mapping capability than the traditional feedforward neural networks (Sigma-Sigma neural networks). In the existing literature, in order to reduce the number of the Pi nodes in the Pi layer, a special multinomial P_…

Paper certifies intersection of minimum-volume confidence sets for multinomial outcomes.

Proved a combinatorial conjecture in machine learning.

FJS method improves multinomial classification accuracy.

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

New method for epidemic model inference using multinomial approximations.

New test for comparing high-dimensional text data.

We derive error estimates for multinomial approximations of American options in a multidimensional jump--diffusion Merton's model. We assume that the payoffs are Markovian and satisfy Lipschitz type conditions. Error estimates for such type of approximations were not obtained before. Our main tool is the strong approxi…

We give an exposition and numerical studies of upper hedging prices in multinomial models from the viewpoint of linear programming and the game-theoretic probability of Shafer and Vovk. We also show that, as the number of rounds goes to infinity, the upper hedging price of a European option converges to the solution of…

Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze the \emph{consistency} and \emph{privacy-preservation}. Instead of deterministic greedy split rule…

A new algorithm reduces the time and space complexity for multinomial logistic bandits.

New algorithm for maximizing revenue in multinomial logistic bandits.

New algorithms improve MPBART for HIV patient data.

New algorithm reduces reinforcement learning regret by adapting to interaction variability.

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

As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Cha…

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

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

In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression. Using a quasi-newton framework we extend this to group-penalized multinomial regression. We give a publicly available implementation for these in R, and compare the speed of this algorithm to a competing algorithm --- w…

Improved regret bound for multinomial logistic bandits with non-linearity.