Tag
This paper proposes Neural-Bayesian Structure Learning, a framework that integrates differentiable structure learning with discrete choice modeling to predict choice behavior and evaluate interventions, achieving comparable performance while recovering coherent dependency structures.
DraftFM is a foundation model for predicting draft picks in Magic: The Gathering, enabling accurate day-zero drafting for new sets by leveraging card features and behavioral data. It demonstrated strong performance on held-out expansions and successfully forecasted an unreleased set.
This paper evaluates four machine learning models for discrete choice modeling in policy preference elicitation, using Monte Carlo experiments and a real energy policy case study to assess performance under individual heterogeneity and choice complexity.
This paper proposes a reformulation to apply tabular foundation models (TFMs) to discrete choice estimation, addressing the structural gap of row-independent assumptions. The best reformulation outperforms hierarchical Bayesian estimation by 8% in holdout log-likelihood and 3.6% in hit rate while running 16 times faster.
This paper proposes a two-stage adapter that embeds foundation model predictions into a multinomial logit model, preserving economic properties like cost monotonicity and interpretable willingness-to-pay while improving accuracy by up to 12.8 percentage points.