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Found 6 papers, 1 papers with code
Tightness of prescriptive tree-based mixed-integer optimization formulations
At an extreme, we prove that this results in ideal formulations for tree ensembles modeling a one-dimensional feature vector.
Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data
This is in contrast to the well-studied setting in which samples of the customer's valuation (willingness to pay) are observed.
Enhancing Counterfactual Classification via Self-Training
We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes with categorical values for finite unseen actions in the observational data to simulate a randomized trial through pseudolabeling, which we refer to as Counterfactual Self-Training (CST).
Loss Functions for Discrete Contextual Pricing with Observational Data
The goal of this paper is to formulate loss functions that can be used for evaluating pricing policies directly from observational data, rather than going through an intermediate demand estimation stage, which may suffer from bias.
Counterfactual Self-Training
We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes for the unseen actions in the observational data to simulate a randomized trial.
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product.
