arXiv：cs.LG· Shivaram Subramanian, Zhengliang Xue, Markus Ettl, Yingdong Lu, Jayant Kalagnanam·· 13 小时前
C3PO：面向离散选择模型的约束三头价格优化网络
Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models
AI 导读
研究者提出 Constrained Triple-Head Price Optimization（C3PO）网络，用于离散选择环境中的实时双层决策，通过基于 Transformer 的架构联合集成价格模仿学习、多任务收入预测与上下文弹性学习，无需重新训练即可跨领域生成定价建议。
正文
Abstract:We propose a Constrained Triple-Head Price Optimization (C3PO) network for real-time bi-level decision making in discrete-choice environments. In these settings, a seller selects product or service offerings to achieve business objectives, while heterogeneous users make personalized acceptance or rejection decisions based on their preferences, ultimately determining seller outcomes. Existing approaches that fine-tune pre-trained tabular foundation models on synthetic data are limited by their imitation-learning paradigm and inability to enforce common pricing rules, including cross-product and global constraints. C3PO addresses these limitations through a novel transformer-based architecture that jointly integrates price imitation learning, multi-task revenue prediction, and in-context elasticity learning to generate robust pricing recommendations across domains without retraining. A lightweight differentiable constraint layer enables generated prices to satisfy business constraints while optimizing the revenue objective. We demonstrate strong out-of-distribution performance on simulated, synthetic, and real-world datasets. Across simulated benchmarks where the calibrated MNL model is misspecified relative to the ground-truth choice model, C3PO improves expected revenue by 1.4 to 7.9 percentage points over a calibrated MNL baseline. The constraint layer reduces average absolute constraint violations by more than 50\%. We further deploy the model in real-world applications, including healthcare tender pricing and airline ancillary pricing, where C3PO improves directional pricing effectiveness, measured by price-increase and price-decrease recall, by 18--34 percentage points depending on the baselines considered, including calibrated MNL, classical optimization, and off-the-shelf tabular foundation-models.
| Subjects: | Machine Learning (cs.LG); Optimization and Control (math.OC) |
| Cite as: | arXiv:2605.06941 [cs.LG] |
| (or arXiv:2605.06941v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06941 arXiv-issued DOI via DataCite |
Submission history
From: Yingdong Lu [view email]
[v1]
Thu, 7 May 2026 20:55:47 UTC (1,034 KB)
[v2]
Thu, 8 Oct 2026 18:22:06 UTC (1,047 KB)
来源：arXiv：cs.LG · arxiv.org