50 citations · 201 across the 25 of their papers we have counts for
5 papers · 1 filter
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Puyu Wang, Yunwen Lei, Yiming Ying +1
Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving min…
Differentially Private Stochastic Gradient Descent with Low-Noise
Puyu Wang, Yunwen Lei, Yiming Ying +1
Modern machine learning algorithms aim to extract fine-grained information from data to provide accurate predictions, which often conflicts with the goal of privacy protection. Thi…
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…
Learning with Average Top-k Loss
Yanbo Fan, Siwei Lyu, Yiming Ying +1
In this work, we introduce the {\em average top-} (\atk) loss as a new aggregate loss for supervised learning, which is the average over the largest individual losses over a…
Online Pairwise Learning Algorithms with Kernels
Yiming Ying, Ding-Xuan Zhou
Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones include ranking, metric learning an…