mlsanityData to 14 Oct 2026

Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design

arXiv 2310.12026 ↗First posted 18 Oct 2023stat.ML cs.LG stat.APcited by 0

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Abstract (arXiv)

Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations.

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References · 11

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  2. 2018Sanity Checks for Saliency Mapscited by 1,156
  3. 2017REBAR: Low-variance, unbiased gradient estimates for discrete latent variable modelscited by 79
  4. 2020Off-policy Bandits with Deficient Supportcited by 61
  5. 2015MuProp: Unbiased Backpropagation for Stochastic Neural Networkscited by 37
  6. 2020DisARM: An Antithetic Gradient Estimator for Binary Latent Variablescited by 28
  7. 2020Probabilistic Best Subset Selection via Gradient-Based Optimizationcited by 17
  8. 2022Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysiscited by 7
  9. 2022Gradient Estimation for Binary Latent Variables via Gradient Variance Clippingcited by 7
  10. 2019Product Aesthetic Design: A Machine Learning Augmentationcited by 4
  11. 2022Active Learning for Non-Parametric Choice Modelscited by 2

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