15 citations · 42 across the 13 of their papers we have counts for
13 papers
Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +7
Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (…
Controllable Gradient Item Retrieval
Haonan Wang, Chang Zhou, Carl Yang +2
In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain att…
Heterogeneous Contrastive Learning
Lecheng Zheng, Jinjun Xiong, Yada Zhu +1
With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multip…
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Yuxin Ma, Arlen Fan, Jingrui He +2
Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult…
Generic Outlier Detection in Multi-Armed Bandit
Yikun Ban, Jingrui He
In this paper, we study the problem of outlier arm detection in multi-armed bandit settings, which finds plenty of applications in many high-impact domains such as finance, healthc…
Continuous Transfer Learning with Label-informed Distribution Alignment
Jun Wu, Jingrui He
Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little i…
Coalesced TLB to Exploit Diverse Contiguity of Memory Mapping
Yikun Ban, Yuchen Zhou, Xu Cheng +1
The miss rate of TLB is crucial to the performance of address translation for virtual memory. To reduce the TLB misses, improving translation coverage of TLB has been an primary ap…
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Jun Wu, Jingrui He, Jiejun Xu
Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn…
Visual Analytics of Anomalous User Behaviors: A Survey
Yang Shi, Yuyin Liu, Hanghang Tong +3
The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it h…
Deep Multimodality Model for Multi-task Multi-view Learning
Lecheng Zheng, Yu Cheng, Jingrui He
Many real-world problems exhibit the coexistence of multiple types of heterogeneity, such as view heterogeneity (i.e., multi-view property) and task heterogeneity (i.e., multi-task…
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching
Yao Zhou, Jingrui He
The unprecedented demand for large amount of data has catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to…
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners
Yao Zhou, Arun Reddy Nelakurthi, Jingrui He
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications. However, most existing works in crowdsourcing…
ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data
Jun Wu, Jingrui He, Yongming Liu
Imbalanced data widely exists in many high-impact applications. An example is in air traffic control, where we aim to identify the leading indicators for each type of accident caus…