A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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.
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…
The paper models network formation using mixed logit models.
Optimal design for multinomial logit models improves assortment selection efficiency.
New algorithm reduces switching costs in multinomial logit bandit problems.
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…
The paper tackles learning mixtures of two multinomial logits, showing identifiability and presenting an algorithm.
Motivated by generating personalized recommendations using ordinal (or preference) data, we study the question of learning a mixture of MultiNomial Logit (MNL) model, a parameterized class of distributions over permutations, from partial ordinal or preference data (e.g. pair-wise comparisons). Despite its long standing…
Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially faster than MCMC at practically no compromises in predict…
The paper proposes a method to test properties of the optimal assortment in multinomial logit models.
New algorithm reduces reinforcement learning regret by adapting to interaction variability.
The paper achieves nearly optimal regret bounds for contextual multinomial logit bandits.
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…
New algorithm for maximizing revenue in multinomial logistic bandits.
Proposes a convex model for mixed logit to handle individual heterogeneity.
Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a -cardinality subset from candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution…
Study optimizes product assortment for retailers with repeated exposures and patience costs.
DMNL bandits optimize assortment choices balancing relevance and diversity.
Two algorithms optimize assortment selection for user choices in unknown MNL models.
We present a mixed multinomial logit (MNL) model, which leverages the truncated stick-breaking process representation of the Dirichlet process as a flexible nonparametric mixing distribution. The proposed model is a Dirichlet process mixture model and accommodates discrete representations of heterogeneity, like a laten…
New algorithm tackles non-linear utility in MNL bandits with regret.
Study dynamic assortment and positioning of products with varying display effects.
New algorithm reduces regret in dynamic assortment selection.
New model improves website ranking by considering user choices as a whole.
Motivated by the phenomenon that companies introduce new products to keep abreast with customers' rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers' preferences through offering recommendations, which may contain existing products and new prod…
Paper presents a privacy-preserving method for dynamic assortment selection.
Study optimizes dynamic product selection and pricing using censored preference feedback.
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional …
New algorithms learn MNL weights efficiently for any slate size.
Discrete choice models are commonly used by applied statisticians in numerous fields, such as marketing, economics, finance, and operations research. When agents in discrete choice models are assumed to have differing preferences, exact inference is often intractable. Markov chain Monte Carlo techniques make approximat…
PASTA optimizes assortment selection using pessimism principle.
We study the active learning problem of top- ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top- items with high probability by adaptively querying sets for comparisons and observing the noisy output of the most preferred item from each comparison. To ac…
Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.
Study optimizes crowdfunding platform offerings based on customer behavior.
Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.
New method estimates sparse covariance matrices in logit mixtures.
In this short note we consider a dynamic assortment planning problem under the capacitated multinomial logit (MNL) bandit model. We prove a tight lower bound on the accumulated regret that matches existing regret upper bounds for all parameters (time horizon , number of items and maximum assortment capacity )…
Dynamic assortment problem on two-sided platform with unknown parameters
We consider the dynamic assortment optimization problem under the multinomial logit model (MNL) with unknown utility parameters. The main question investigated in this paper is model mis-specification under the -contamination model, which is a fundamental model in robust statistics and machine learning. In…
The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to …
This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between …
We analyze mixing times of three DA algorithms for regression models.
We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Since all the utility parameters of MNL are unknown, the selle…
We improve Gaussian copula models for imputing mixed data types with precise approximations.
Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often fragile and can be defended via image-level processing, it remains unclear how high-level features are perturbed by such attacks. We investigate…
The vast majority of the neural network literature focuses on predicting point values for a given set of response variables, conditioned on a feature vector. In many cases we need to model the full joint conditional distribution over the response variables rather than simply making point predictions. In this paper, we …
New algorithms improve MPBART for HIV patient data.