Discrete Choice Models
14 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Enhancing Discrete Choice Models with Representation Learning
In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates.
Choice Set Optimization Under Discrete Choice Models of Group Decisions
tomlinsonk/choice-set-opt
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ICML 2020
The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set.
A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability
Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e. g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge.
Robust discrete choice models with t-distributed kernel errors
In a case study on transport mode choice behaviour, MNR and Gen-MNR outperform MNP by substantial margins in terms of in-sample fit and out-of-sample predictive accuracy.
Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard.
Choice Set Confounding in Discrete Choice
tomlinsonk/choice-set-confounding
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Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alternatives (the choice set).
Identification of Incomplete Preferences
We provide a sharp identification region for discrete choice models where consumers' preferences are not necessarily complete and only aggregate choice data is available.
Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance
ioaar/interpretable-embeddings-mnl
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The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative.
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
zhengyunhan/equality-of-opportunity-travel-behavior
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Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms.
Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling
xingjian-zhang/fast-partial-ranking-mnl
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The problem of learning mixture of MNL models from partial rankings naturally arises in such applications.
