Adam Elmachtoub, Columbia University Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is predict-then-optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in the downstream […]

Speaker: Saeed Ghadimi, Princeton University Abstract In this talk, we propose and analyze derivative-free stochastic approximation algorithms for nonconvex optimization. We first propose generalization of the conditional gradient algorithm achieving a similar rate to the standard stochastic gradient algorithm (SGD) using only noisy function evaluations (zeroth-order information). For the high-dimensional setting, we explore the advantage […]

Speaker: Adam Diamand Abstract: We investigate the scheduling practices of multistage outpatient health programs that offer care plans customized to the needs of their patients. We formulate the scheduling problem as a Markov decision process (MDP) where patients can reschedule their appointment, may fail to show up, and may even become ineligible. The MDP has […]