22 citations · 48 across the 8 of their papers we have counts for
6 papers · 1 filter
Homophily Outlier Detection in Non-IID Categorical Data
Guansong Pang, Longbing Cao, Ling Chen
Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Indepen…
Unified Robust Training for Graph NeuralNetworks against Label Noise
Yayong Li, Jie yin, Ling Chen
Graph neural networks (GNNs) have achieved state-of-the-art performance for node classification on graphs. The vast majority of existing works assume that genuine node labels are a…
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Yunqiu Xu, Meng Fang, Ling Chen +3
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to repr…
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs
Yayong Li, Jie Yin, Ling Chen
Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label…
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Guansong Pang, Longbing Cao, Ling Chen +1
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods t…
On the Convergence of A Family of Robust Losses for Stochastic Gradient Descent
Bo Han, Ivor W. Tsang, Ling Chen
The convergence of Stochastic Gradient Descent (SGD) using convex loss functions has been widely studied. However, vanilla SGD methods using convex losses cannot perform well with…