Search Results for author:
Found 4 papers, 1 papers with code
Self-adapting Robustness in Demand Learning
We also show that ARL, by being conscious of both model ambiguity and revenue, bridges the gap between a distributionally robust policy and a follow-the-leader policy, which focus on model ambiguity and revenue, respectively.
Self-guided Approximate Linear Programs
Approximate linear programs (ALPs) are well-known models based on value function approximations (VFAs) to obtain policies and lower bounds on the optimal policy cost of discounted-cost Markov decision processes (MDPs).
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage
Models of user behavior are critical inputs in many prescriptive settings and can be viewed as decision rules that transform state information available to the user into actions.
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
We design a stochastic feasible level set method (SFLS) for SOECs that has low data complexity and emphasizes feasibility before convergence.
