163 citations · 1.2k across the 128 of their papers we have counts for
14 papers · 1 filter
A Novel Convergence Analysis for Algorithms of the Adam Family
Zhishuai Guo, Yi Xu, Wotao Yin +2
Since its invention in 2014, the Adam optimizer has received tremendous attention. On one hand, it has been widely used in deep learning and many variants have been proposed, while…
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
Dixian Zhu, Yiming Ying, Tianbao Yang
We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification that are formulated from distributionally robust optimization (D…
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
Zhenhuan Yang, Yunwen Lei, Puyu Wang +2
Pairwise learning refers to learning tasks where the loss function depends on a pair of instances. It instantiates many important machine learning tasks such as bipartite ranking a…
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities
Tianbao Yang
In this extended abstract, we will present and discuss opportunities and challenges brought about by a new deep learning method by AUC maximization (aka \underline{\bf D}eep \under…
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
Guanghui Wang, Ming Yang, Lijun Zhang +1
In this paper, we study stochastic optimization of areas under precision-recall curves (AUPRC), which is widely used for combating imbalanced classification tasks. Although a few m…
Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimization
Zhishuai Guo, Quanqi Hu, Lijun Zhang +1
In this paper, we consider non-convex stochastic bilevel optimization (SBO) problems that have many applications in machine learning. Although numerous studies have proposed stocha…