arXiv research

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

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

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…

New method for epidemic model inference using multinomial approximations.

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

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

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

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

New test for comparing high-dimensional text data.

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

New algorithm for maximizing revenue in multinomial logistic bandits.

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative model with multinomial lik…

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

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

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…

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…

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

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.

Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution…

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 …

New model improves website ranking by considering user choices as a whole.

Researchers show mixtures of ranking models are generally identifiable.

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…

Improved regret bounds for logistic bandits via novel confidence set construction.

New method tests risk measures for various distortions.

New algorithm reduces regret in dynamic assortment selection.

Dynamic assortment problem on two-sided platform with unknown parameters

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven…

Improved online confidence bounds for multinomial logistic models in bandits.

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.

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…

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…

Two algorithms optimize assortment selection for user choices in unknown MNL models.

Improved logistic MoE with sigmoid gate shows better sample efficiency.

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.

New conjugate priors improve Bayesian inference for multinomial probit models.