80 citations · 108 across the 11 of their papers we have counts for
8 papers · 1 filter
Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors
Xueying Ding, Rui Xi, Leman Akoglu
The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focuse…
Descriptive Kernel Convolution Network with Improved Random Walk Kernel
Meng-Chieh Lee, Lingxiao Zhao, Leman Akoglu
Graph kernels used to be the dominant approach to feature engineering for structured data, which are superseded by modern GNNs as the former lacks learnability. Recently, a suite o…
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
Konstantinos Sotiropoulos, Lingxiao Zhao, Pierre Jinghong Liang +1
Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances? Many real-world pro…
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities
Leman Akoglu, Jaemin Yoo
Self-supervised learning (SSL) is a growing torrent that has recently transformed machine learning and its many real world applications, by learning on massive amounts of unlabeled…
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
Jaemin Yoo, Yue Zhao, Lingxiao Zhao +1
Self-supervised learning (SSL) has proven effective in solving various problems by generating internal supervisory signals. Unsupervised anomaly detection, which faces the high cos…
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management
Xueying Ding, Nikita Seleznev, Senthil Kumar +2
Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few. While the literature is ab…