12 citations · 33 across the 11 of their papers we have counts for
11 papers
On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning
Bokun Wang, Yunwen Lei, Yiming Ying +1
We study the discriminative probabilistic modeling on a continuous domain for the data prediction task of (multimodal) self-supervised representation learning. To address the chall…
Fairness-aware Differentially Private Collaborative Filtering
Zhenhuan Yang, Yingqiang Ge, Congzhe Su +3
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes w…
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence
Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney +1
The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups d…
AUC Maximization in the Era of Big Data and AI: A Survey
Tianbao Yang, Yiming Ying
Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that…
Differentially Private SGDA for Minimax Problems
Zhenhuan Yang, Shu Hu, Yunwen Lei +3
Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descen…
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…
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
Zhuoning Yuan, Zhishuai Guo, Yi Xu +2
Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification. However, the research on…
Stochastic Hard Thresholding Algorithms for AUC Maximization
Zhenhuan Yang, Baojian Zhou, Yunwen Lei +1
In this paper, we aim to develop stochastic hard thresholding algorithms for the important problem of AUC maximization in imbalanced classification. The main challenge is the pairw…
Learning by Minimizing the Sum of Ranked Range
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…
Online AUC Optimization for Sparse High-Dimensional Datasets
Baojian Zhou, Yiming Ying, Steven Skiena
The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is ab…
Faster Convergence of a Randomized Coordinate Descent Method for Linearly Constrained Optimization Problems
Qin Fan, Min Xu, Yiming Ying
The problem of minimizing a separable convex function under linearly coupled constraints arises from various application domains such as economic systems, distributed control, and…