Finite-Sample Performance Guarantee

Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances

  • Jie Wang
    • Categories Optimization in Data Science Tags , ,

    Optimal transport has been very successful for various machine learning tasks; however, it is known to suffer from the curse of dimensionality. Hence, dimensionality reduction is desirable when applied to high-dimensional data with low-dimensional structures. The kernel max-sliced (KMS) Wasserstein distance is developed for this purpose by finding an optimal nonlinear mapping that reduces data … Read more

    Data-Driven Counterfactual Optimization For Personalized Clinical Decision-Making

  • Che-Yi Liao
  • Esmaeil Keyvanshokooh
  • Gian-Gabriel Garcia
    • Categories Applications - OR and Management Sciences, Optimization in Data Science Tags , , , ,

    Chronic diseases have a significant impact on global mortality rates and healthcare costs. Notably, machine learning-based clinical assessment tools are becoming increasingly popular for informing treatment targets for high-risk patients with chronic diseases. However, using these tools alone, it is challenging to identify personalized treatment targets that lower the risks of adverse outcomes to a … Read more