30 citations · 148 across the 30 of their papers we have counts for
6 papers · 1 filter
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…
Stability and Generalization for Randomized Coordinate Descent
Puyu Wang, Liang Wu, Yunwen Lei
Randomized coordinate descent (RCD) is a popular optimization algorithm with wide applications in solving various machine learning problems, which motivates a lot of theoretical an…
Fine-grained Generalization Analysis of Structured Output Prediction
Waleed Mustafa, Yunwen Lei, Antoine Ledent +1
In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains whe…
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…
Fine-grained Generalization Analysis of Vector-valued Learning
Liang Wu, Antoine Ledent, Yunwen Lei +1
Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although…
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…