Publications

TOD: GPU-accelerated Outlier Detection via Tensor Operations

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The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies

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ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels

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ADBench: Anomaly Detection Benchmark

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BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

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ELECT: Toward Unsupervised Outlier Model Selection

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Artificial Intelligence Foundation for Therapeutic Science

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ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

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Contrastive Attributed Network Anomaly Detection with Data Augmentation

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Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

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Revisiting Time Series Outlier Detection: Definitions and Benchmarks

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Automatic Unsupervised Outlier Model Selection

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SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection

Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake …
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TODS: An Automated Time Series Outlier Detection System

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AutoAudit: Mining Accounting and Time-Evolving Graphs

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A Data Denoising Approach to Optimize Functional Clustering of Single Cell RNA-sequencing Data

DOI IEEE Xplore

COPOD: Copula-Based Outlier Detection

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing unsupervised …
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SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from …
PDF Code DOI IEEE Xplore

DSR: An Accurate Single Image Super Resolution Approach for Various Degradations

Recently, convolution neural networks based approaches have achieved unprecedented success for image super resolution. However, such …
PDF DOI IEEE Xplore

SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula

Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at …
PDF Code PPAI Arxiv

Combining Machine Learning Models Using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry …
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PyOD: A Python Toolbox for Scalable Outlier Detection

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a …
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Music Artist Classification with Convolutional Recurrent Neural Networks

Previous attempts at music artist classification use frame level audio features which summarize frequency content within short …
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LSCP: Locally Selective Combination in Parallel Outlier Ensembles

In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. …
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XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and …
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DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

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Employee Turnover Prediction with Machine Learning: A Reliable Approach

Supervised machine learning methods are described, demonstrated and assessed for the prediction of employee turnover within an …
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An empirical study of touch-based authentication methods on smartwatches

The emergence of smartwatches poses new challenges to information security. Although there are mature touch-based authentication …
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