The problems of online pricing with offline data, or other similar general online decision making with offline data problems, have observed increasing interests especially in operational research. To evaluate pricing policies when offline data are available, the decision maker can either...

In real-time decision-making problems for complicated stochastic systems, a covariate that reflects the state of the system is observed in real time and a state-dependent decision needs to be made immediately to optimize some system performance. Such system performances, for complicated...

This study investigates the machine learning tools that are employed to learn data-to-decision mappings. A data-to-decision mapping, once learnt, adopts relevant operational information as input and effectively outputs a reliable decision variable. The operational data used by the machine...

We consider a single-product dynamic pricing problem under a specific non-stationary setting, where the underlying demand process grows over time in expectation and also possibly in the level of random fluctuation. The decision maker sequentially sets price in each time period and learns the...

The two roles of a dealer in over-the-counter markets, immediacy provision and matchmaking, create a conflict of interest--the dealer prioritizes inventory turnover for immediacy provision, rather than making matches between customer flows. Compared to a counterfactual scenario without this...

We develop an analytical framework to appropriately model and adequately analyze A/B tests in presence of nonparametric non-stationarities in the targeted business metrics. A/B tests, also known as online randomized controlled experiments, have been used at scale by data-driven enterprises to...