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08/28/2023
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities
Self-supervised learning (SSL) is a growing torrent that has recently tr...
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04/06/2023
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management
Anomalies are often indicators of malfunction or inefficiency in various...
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11/05/2022
Unsupervised Machine Learning for Explainable Medicare Fraud Detection
The US federal government spends more than a trillion dollars per year o...
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10/15/2022
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments
How can we detect outliers, both scattered and clustered, and also expli...
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08/16/2022
Role of Data Augmentation in Unsupervised Anomaly Detection
Self-supervised learning (SSL) has emerged as a promising alternative to...
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06/15/2022
Summarizing Labeled Multi-Graphs
Real-world graphs can be difficult to interpret and visualize beyond a c...
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06/02/2022
Sparx: Distributed Outlier Detection at Scale
There is no shortage of outlier detection (OD) algorithms in the literat...
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11/11/2021
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
Given a cardiac-arrest patient being monitored in the ICU (intensive car...
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10/13/2021
C-AllOut: Catching Calling Outliers by Type
Given an unlabeled dataset, wherein we have access only to pairwise simi...
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05/21/2021
Anomaly Mining – Past, Present and Future
Anomaly mining is an important problem that finds numerous applications ...
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12/05/2020
FAIROD: Fairness-aware Outlier Detection
Fairness and Outlier Detection (OD) are closely related, as it is exactl...
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10/07/2020
Anomaly Detection in Large Labeled Multi-Graph Databases
Within a large database G containing graphs with labeled nodes and direc...
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06/22/2020
Connecting Graph Convolutional Networks and Graph-Regularized PCA
Graph convolution operator of the GCN model is originally motivated from...
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06/20/2020
Generalizing Graph Neural Networks Beyond Homophily
We investigate the representation power of graph neural networks in the ...
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11/06/2019
Coverage-based Outlier Explanation
Outlier detection is a core task in data mining with a plethora of algor...
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09/26/2019
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification
Semi-supervised learning (SSL) is effectively used for numerous classifi...
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09/26/2019
PairNorm: Tackling Oversmoothing in GNNs
The performance of graph neural nets (GNNs) is known to gradually decrea...
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07/08/2019
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
Nearest-neighbor (NN) procedures are well studied and widely used in bot...
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06/28/2019
Continual Rare-Class Recognition with Emerging Novel Subclasses
Given a labeled dataset that contains a rare (or minority) class of of-i...
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05/06/2018
Incorporating Privileged Information to Unsupervised Anomaly Detection
We introduce a new unsupervised anomaly detection ensemble called SPI wh...
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08/20/2017
Explaining Anomalies in Groups with Characterizing Subspace Rules
Anomaly detection has numerous applications and has been studied vastly....
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09/18/2016
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
Ensemble methods for classification and clustering have been effectively...
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11/19/2015
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
Review fraud is a pervasive problem in online commerce, in which fraudul...
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11/27/2013