198 citations · 480 across the 36 of their papers we have counts for
14 papers · 1 filter
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game
Ke Ma, Qianqian Xu, Jinshan Zeng +3
Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has…
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting
Yunxin Liu, Qiaosi Yi, Jinshan Zeng
The lightweight crowd counting models, in particular knowledge distillation (KD) based models, have attracted rising attention in recent years due to their superiority on computati…
Poisoning Attack against Estimating from Pairwise Comparisons
Ke Ma, Qianqian Xu, Jinshan Zeng +2
As pairwise ranking becomes broadly employed for elections, sports competitions, recommendations, and so on, attackers have strong motivation and incentives to manipulate the ranki…
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Jinshan Zeng, Min Zhang, Shao-Bo Lin
Boosting is a well-known method for improving the accuracy of weak learners in machine learning. However, its theoretical generalization guarantee is missing in literature. In this…
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Ke Ma, Jinshan Zeng, Qianqian Xu +3
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi…
Fast Polynomial Kernel Classification for Massive Data
Jinshan Zeng, Minrun Wu, Shao-Bo Lin +1
In the era of big data, it is desired to develop efficient machine learning algorithms to tackle massive data challenges such as storage bottleneck, algorithmic scalability, and in…
Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces
Yanwei Fu, Chen Liu, Donghao Li +4
The great success of deep neural networks is built upon their over-parameterization, which smooths the optimization landscape without degrading the generalization ability. Despite…
On ADMM in Deep Learning: Convergence and Saturation-Avoidance
Jinshan Zeng, Shao-Bo Lin, Yuan Yao +1
In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \textit{sigmoid-ADM…
Learning rates for classification with Gaussian kernels
Shao-Bo Lin, Jinshan Zeng, Xiangyu Chang
This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss. Our first result shows that for some lo…
Constructive neural network learning
Shaobo Lin, Jinshan Zeng, Xiaoqin Zhang
In this paper, we aim at developing scalable neural network-type learning systems. Motivated by the idea of "constructive neural networks" in approximation theory, we focus on "con…
Greedy Criterion in Orthogonal Greedy Learning
Lin Xu, Shaobo Lin, Jinshan Zeng +2
Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then bu…
Model selection of polynomial kernel regression
Shaobo Lin, Xingping Sun, Zongben Xu +1
Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regu…
Greedy metrics in orthogonal greedy learning
Lin Xu, Shaobo Lin, Jinshan Zeng +1
Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a new atom from a dictionary via the steepest gradient descent and build the estimator via orthogonal proje…
Learning rates of coefficient regularization learning with Gaussian kernel
Shaobo Lin, Jinshan Zeng, Jian Fang +1
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and regularization schemes with are central in use. It is…