24 citations · 32 across the 7 of their papers we have counts for
7 papers
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…
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization
Jinshan Zeng, Yixuan Zha, Ke Ma +1
The low-rank stochastic semidefinite optimization has attracted rising attention due to its wide range of applications. The nonconvex reformulation based on the low-rank factorizat…
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…
Robust Ordinal Embedding from Contaminated Relative Comparisons
Ke Ma, Qianqian Xu, Xiaochun Cao
Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned fr…
Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin
Ke Ma, Qianqian Xu, Zhiyong Yang +1
In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These…
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction
Jinshan Zeng, Ke Ma, Yuan Yao
There is a recent surge of interest in nonconvex reformulations via low-rank factorization for stochastic convex semidefinite optimization problem in the purpose of efficiency and…