Bayesian Deep Learning for Discrete Choice

• URL: http://arxiv.org/abs/2505.18077v1
• Date: Fri, 23 May 2025 16:33:47 GMT
• Title: Bayesian Deep Learning for Discrete Choice
• Authors: Daniel F. Villarraga, Ricardo A. Daziano,
• Abstract summary: We introduce a deep learning model architecture specifically designed to integrate approximate Bayesian methods.<n>Our proposed model collapses to behaviorally informed hypotheses when data is limited.<n>We present results from two empirical case studies: one using revealed mode choice data in NYC, and the other based on the widely used Swiss train choice stated preference data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections, and consumer preferences. DCMs play a central role in applied econometrics by enabling inference on key economic variables, such as marginal rates of substitution, rather than focusing solely on predicting choices on new unlabeled data. However, while traditional DCMs offer high interpretability and support for point and interval estimation of economic quantities, these models often underperform in predictive tasks compared to deep learning (DL) models. Despite their predictive advantages, DL models remain largely underutilized in discrete choice due to concerns about their lack of interpretability, unstable parameter estimates, and the absence of established methods for uncertainty quantification. Here, we introduce a deep learning model architecture specifically designed to integrate with approximate Bayesian inference methods, such as Stochastic Gradient Langevin Dynamics (SGLD). Our proposed model collapses to behaviorally informed hypotheses when data is limited, mitigating overfitting and instability in underspecified settings while retaining the flexibility to capture complex nonlinear relationships when sufficient data is available. We demonstrate our approach using SGLD through a Monte Carlo simulation study, evaluating both predictive metrics--such as out-of-sample balanced accuracy--and inferential metrics--such as empirical coverage for marginal rates of substitution interval estimates. Additionally, we present results from two empirical case studies: one using revealed mode choice data in NYC, and the other based on the widely used Swiss train choice stated preference data.

Related papers

• On Equivariant Model Selection through the Lens of Uncertainty [49.137341292207]
  Equivariant models leverage prior knowledge on symmetries to improve predictive performance, but misspecified architectural constraints can harm it instead.<n>We compare frequentist (via Conformal Prediction), Bayesian (via the marginal likelihood), and calibration-based measures to naive error-based evaluation.<n>We find that uncertainty metrics generally align with predictive performance, but Bayesian model evidence does so inconsistently.
  arXiv  Detail & Related papers  (2025-06-23T13:35:06Z)
• Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty [9.176056742068814]
  This work investigates the use of uncertainty in the selection of data points to rehearse in online learning when storage capacity is constrained.<n>We present three novel approaches: the Threshold method, which excludes samples with uncertainty below a specified threshold, the Greedy method, designed to maximize uncertainty among the stored points, and Threshold-Greedy, which combines the previous two approaches.
  arXiv  Detail & Related papers  (2025-04-06T18:48:55Z)
• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective.<n>The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.<n>The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements [57.04370580292727]
  We consider data-driven Bayesian state estimation from compressed measurements.<n>The dimension of the temporal measurement vector is lower than that of the temporal state vector to be estimated.<n>The underlying dynamical model of the state's evolution is unknown for a'model-free process'
  arXiv  Detail & Related papers  (2024-07-10T05:03:48Z)
• Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models [0.9940108090221528]
  Aggregation strategies have been developed to pool or fuse the weights and biases of distributed deterministic models. We show that simple application of the aggregation methods associated with FL schemes for deterministic models is either impossible or results in sub-optimal performance.
  arXiv  Detail & Related papers  (2024-03-22T15:02:24Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation [23.667430143035787]
  We propose a novel MOdality DEcorrelating STable learning framework, MODEST for brevity, to learn users' stable preference. Inspired by sample re-weighting techniques, the proposed method aims to estimate a weight for each item, such that the features from different modalities in the weighted distribution are decorrelated. Our method could be served as a play-and-plug module for existing multimedia recommendation backbones.
  arXiv  Detail & Related papers  (2023-06-25T09:09:11Z)
• Learning Robust Statistics for Simulation-based Inference under Model Misspecification [23.331522354991527]
  We propose the first general approach to handle model misspecification that works across different classes of simulation-based inference methods. We show that our method yields robust inference in misspecified scenarios, whilst still being accurate when the model is well-specified.
  arXiv  Detail & Related papers  (2023-05-25T09:06:26Z)
• Uncertainty-guided Source-free Domain Adaptation [77.3844160723014]
  Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.
  arXiv  Detail & Related papers  (2022-08-16T08:03:30Z)
• Revisiting Design Choices in Model-Based Offline Reinforcement Learning [39.01805509055988]
  Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies. This paper compares and designs novel protocols to investigate their interaction with other hyper parameters, such as the number of models, or imaginary rollout horizon.
  arXiv  Detail & Related papers  (2021-10-08T13:51:34Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Learnable Bernoulli Dropout for Bayesian Deep Learning [53.79615543862426]
  Learnable Bernoulli dropout (LBD) is a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters. LBD leads to improved accuracy and uncertainty estimates in image classification and semantic segmentation.
  arXiv  Detail & Related papers  (2020-02-12T18:57:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.