91 citations · 255 across the 15 of their papers we have counts for
15 papers
Online semi-supervised perception: Real-time learning without explicit feedback
Branislav Kveton, Michal Valko, Matthai Phillipose +1
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In pa…
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Han Zhang, Wenhao Zheng, Charley Chen +4
Since the label collecting is prohibitive and time-consuming, unsupervised methods are preferred in applications such as fraud detection. Meanwhile, such applications usually requi…
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
Shun Zheng, Xu Han, Yankai Lin +5
Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these meth…
No Place to Hide: Catching Fraudulent Entities in Tensors
Yikun Ban, Xin Liu, Yitao Duan +2
Many approaches focus on detecting dense blocks in the tensor of multimodal data to prevent fraudulent entities (e.g., accounts, links) from retweet boosting, hashtag hijacking, li…
BadLink: Combining Graph and Information-Theoretical Features for Online Fraud Group Detection
Yikun Ban, Xin Liu, Tianyi Zhang +4
Frauds severely hurt many kinds of Internet businesses. Group-based fraud detection is a popular methodology to catch fraudsters who unavoidably exhibit synchronized behaviors. We…
EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses
Mikhail Kazdagli, Ling Huang, Vijay Reddi +1
Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs agains…