163 citations · 1.6k across the 92 of their papers we have counts for
7 papers · 1 filter
Stagewise Training Accelerates Convergence of Testing Error Over SGD
Zhuoning Yuan, Yan Yan, Rong Jin +1
Stagewise training strategy is widely used for learning neural networks, which runs a stochastic algorithm (e.g., SGD) starting with a relatively large step size (aka learning rate…
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin +2
Difference of convex (DC) functions cover a broad family of non-convex and possibly non-smooth and non-differentiable functions, and have wide applications in machine learning and…
Large-scale Distance Metric Learning with Uncertainty
Qi Qian, Jiasheng Tang, Hao Li +2
Distance metric learning (DML) has been studied extensively in the past decades for its superior performance with distance-based algorithms. Most of the existing methods propose to…
Learning with Non-Convex Truncated Losses by SGD
Yi Xu, Shenghuo Zhu, Sen Yang +3
Learning with a {\it convex loss} function has been a dominating paradigm for many years. It remains an interesting question how non-convex loss functions help improve the generali…
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang +2
Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received incre…
Robust Optimization over Multiple Domains
Qi Qian, Shenghuo Zhu, Jiasheng Tang +3
In this work, we study the problem of learning a single model for multiple domains. Unlike the conventional machine learning scenario where each domain can have the corresponding m…