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A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
Our meta-policy is flexible enough to be applied to a general inventory systems framework covering a wide range of inventory management problems with myopic clairvoyant optimal policy.
Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
Under the \alpha-global strong convexity condition, we demonstrate that the worst-case regret of any data-driven method is lower bounded by \Omega(\log T/\alpha), which is the first lower bound result that matches the existing upper bound with respect to both parameter \alpha and time horizon T. Along the way, we propose to analyze the SAA regret via a new gradient approximation technique, as well as a new class of smooth inverted-hat-shaped hard problem instances that might be of independent interest for the lower bounds of broader data-driven problems.
Stochastic Constrained DRO with a Complexity Independent of Sample Size
Distributionally Robust Optimization (DRO), as a popular method to train robust models against distribution shift between training and test sets, has received tremendous attention in recent years.
Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing
We introduce the regularized revenue, i. e., the total revenue with a balancing regularization, as our objective to incorporate fair resource-consumption balancing into the revenue maximization goal.
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret.
