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Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data
Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion.
Optimal Transport Relaxations with Application to Wasserstein GANs
We propose a family of relaxations of the optimal transport problem which regularize the problem by introducing an additional minimization step over a small region around one of the underlying transporting measures.
Waterfall Bandits: Learning to Sell Ads Online
We design an online learning algorithm for solving this problem, which interleaves learning and optimization, and prove that this algorithm has sublinear regret.
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