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Found 5 papers, 1 papers with code
How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility?
We focus on incorporating monotonic trends, and propose a novel gradient-based point-wise loss function for enforcing partial monotonicity with deep neural networks.
Dynamic Pricing for Airline Ancillaries with Customer Context
We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website.
Adaptive Model Selection Framework: An Application to Airline Pricing
Multiple machine learning and prediction models are often used for the same prediction or recommendation task.
Negotiating Networks in Oligopoly Markets for Price-Sensitive Products
We present a novel framework to learn functions that estimate decisions of sellers and buyers simultaneously in an oligopoly market for a price-sensitive product.
Distribution Shift in Airline Customer Behavior during COVID-19
Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training.
