2 citations · 3 across the 2 of their papers we have counts for
6 papers · 1 filter
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Zhuoning Yuan, Yan Yan, Milan Sonka +1
Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focu…
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise
Yan Yan, Xin Man, Tianbao Yang
In this paper, we propose robust stochastic algorithms for solving convex compositional problems of the form $f(\E_ξg(\cdot; ξ)) + r(\cdot)$ by establishing {\bf sub-Gaussian confi…
A Simple and Effective Framework for Pairwise Deep Metric Learning
Qi Qi, Yan Yan, Xiaoyu Wang +1
Deep metric learning (DML) has received much attention in deep learning due to its wide applications in computer vision. Previous studies have focused on designing complicated loss…
Stochastic Optimization for Non-convex Inf-Projection Problems
Yan Yan, Yi Xu, Lijun Zhang +2
In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint…
Stochastic Primal-Dual Algorithms with Faster Convergence than for Problems without Bilinear Structure
Yan Yan, Yi Xu, Qihang Lin +2
Previous studies on stochastic primal-dual algorithms for solving min-max problems with faster convergence heavily rely on the bilinear structure of the problem, which restricts th…
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
Yan Yan, Tianbao Yang, Zhe Li +2
Stochastic momentum methods have been widely adopted in training deep neural networks. However, their theoretical analysis of convergence of the training objective and the generali…