50 citations · 201 across the 25 of their papers we have counts for
5 papers · 1 filter
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…
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
Bokun Wang, Zhuoning Yuan, Yiming Ying +1
In recent years, model-agnostic meta-learning (MAML) has become a popular research area. However, the stochastic optimization of MAML is still underdeveloped. Existing MAML algorit…
Sum of Ranked Range Loss for Supervised Learning
Shu Hu, Yiming Ying, Xin Wang +1
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual l…
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
Yunwen Lei, Zhenhuan Yang, Tianbao Yang +1
Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few. A…
Differentially Private SGD with Non-Smooth Losses
Puyu Wang, Yunwen Lei, Yiming Ying +1
In this paper, we are concerned with differentially private {stochastic gradient descent (SGD)} algorithms in the setting of stochastic convex optimization (SCO). Most of the exist…