2020/02/20 by Qijia Jiang, Jiang, Qijia, Olaoluwa Adigun +8 · 1 citation
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2002.08605
arxiv created 2020/02/20 · openalex publication_date 2020/02/20 · arxiv updated 2020/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surrogates. We pose the training problem as an optimization over a relaxed surrogate space, which we solve by estimating local gradients for the metric and performing inexact convex projections. We analyze gradient estimates based on finite differences and local linear interpolations, and show convergence of our approach under smoothness assumptions with respect to the surrogates. Experimental results on classification and ranking problems verify the proposal performs on par with methods that know the mathematical formulation, and adds notable value when the form of the metric is unknown.