2017/07/06 by Hyoung-seok Kim, HyoungSeok Kim, JiHoon Kang +17
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Matrix Theory and Algorithms #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques #cs.AI #cs.LG #math.OC #stat.ML
paper · pdf · doi:10.48550/arxiv.1707.01647
arxiv created 2017/07/06 · openalex publication_date 2017/07/06 · arxiv updated 2017/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm. By inspecting the differences between the regret bounds of traditional algorithms and adaptive one, we provide a guide for choosing an optimizer with respect to the given data set and the loss function. For analysis, we assume that the loss function is convex and its gradient is Lipschitz continuous.