2018/05/27 by Sen Zhao, Mahdi Milani Fard, Zhao, Sen +6 · 1 citation
Computer Science · Mathematics · #Advanced Neural Network Applications #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1805.10582
Proceedings of the 36th International Conference on Machine Learning (ICML'19)
openalex publication_date 2018/05/27 · arxiv created 2019/06/15 · arxiv updated 2019/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed. Motivated by known connections between complex test metrics and cost-weighted learning, we propose addressing these issues by using a weighted loss function with a standard loss, where the weights on the training examples are learned to optimize the test metric on a validation set. These metric-optimized example weights can be learned for any test metric, including black box and customized ones for specific applications. We illustrate the performance of the proposed method on diverse public benchmark datasets and real-world applications. We also provide a generalization bound for the method.