arXiv research

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.

Proposes PG-DA for Bayesian MMNL estimation to handle non-conjugacy.

The paper models network formation using mixed logit models.

Optimal design for multinomial logit models improves assortment selection efficiency.

VB methods improve MMNL estimation speed and accuracy.

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…

Logit-Coordinate models improve representation of mixed data types.

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.

A model for learning customer preferences in a dynamic product launch setting.

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 $K$-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…

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 $ilde{O}(\sqrt{T})$ regret.

Study dynamic assortment and positioning of products with varying display effects.

New algorithm reduces regret in dynamic assortment selection.

New policy optimizes product assortment in the presence of unpredictable customers.

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

Generative model captures how individuals process travel information under uncertainty.

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-$k$ ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top-$k$ 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…

New algorithms minimize regret in combinatorial online learning with relative feedback.

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.

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 $T$, number of items $N$ and maximum assortment capacity $K$)…

New method estimates sparse covariance matrices in logit mixtures.

Dynamic assortment problem on two-sided platform with unknown parameters

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 …

A two-layer network corrects logits to defend adversarial attacks.

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 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 analyze mixing times of three DA algorithms for regression models.