Yue Zhao

CoRR, 2023

Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Weakly Supervised Anomaly Detection: A Survey.

[BibT_eX]

[DOI]

CoRR, 2023

DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

ADGym: Design Choices for Deep Anomaly Detection.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

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

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

TOD: GPU-accelerated Outlier Detection via Tensor Operations.

[BibT_eX]

[DOI]

George H. Chen

Zhihao Jia

Proc. VLDB Endow., 2022

Diffusion Models: A Comprehensive Survey of Methods and Applications.

[BibT_eX]

[DOI]

CoRR, 2022

Towards Unsupervised HPO for Outlier Detection.

[BibT_eX]

[DOI]

CoRR, 2022

Benchmarking Node Outlier Detection on Graphs.

[BibT_eX]

[DOI]

CoRR, 2022

ADBench: Anomaly Detection Benchmark.

[BibT_eX]

[DOI]

CoRR, 2022

PyGOD: A Python Library for Graph Outlier Detection.

[BibT_eX]

[DOI]

CoRR, 2022

Contrastive Attributed Network Anomaly Detection with Data Augmentation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

ADBench: Anomaly Detection Benchmark.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Toward Unsupervised Outlier Model Selection.

[BibT_eX]

[DOI]

Sean Zhang

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

TOD: Tensor-based Outlier Detection.

[BibT_eX]

[DOI]

George H. Chen

Zhihao Jia

CoRR, 2021

A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?

[BibT_eX]

[DOI]

CoRR, 2021

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics.

[BibT_eX]

[DOI]

CoRR, 2021

PyHealth: A Python Library for Health Predictive Models.

[BibT_eX]

[DOI]

CoRR, 2021

Automatic Unsupervised Outlier Model Selection.

[BibT_eX]

[DOI]

Ryan A. Rossi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Revisiting Time Series Outlier Detection: Definitions and Benchmarks.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

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

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection.

[BibT_eX]

[DOI]

Proceedings of Machine Learning and Systems 2021, 2021

TODS: An Automated Time Series Outlier Detection System.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Automating Outlier Detection via Meta-Learning.

[BibT_eX]

[DOI]

Ryan A. Rossi

CoRR, 2020

SUOD: A Scalable Unsupervised Outlier Detection Framework.

[BibT_eX]

[DOI]

CoRR, 2020

SUOD: Toward Scalable Unsupervised Outlier Detection.

[BibT_eX]

[DOI]

CoRR, 2020

Dsr: An Accurate Single Image Super Resolution Approach For Various Degradations.

[BibT_eX]

[DOI]

Yiqun Mei

Wei Liang

Proceedings of the IEEE International Conference on Multimedia and Expo, 2020

SynC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources.

[BibT_eX]

[DOI]

Jialin Fu

Proceedings of the 20th International Conference on Data Mining Workshops, 2020

COPOD: Copula-Based Outlier Detection.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

AutoAudit: Mining Accounting and Time-Evolving Graphs.

[BibT_eX]

[DOI]

Meng-Chieh Lee

Aluna Wang

Pierre Jinghong Liang

Vincent S. Tseng

Christos Faloutsos

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

A data denoising approach to optimize functional clustering of single cell RNA-sequencing data.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

Combining Machine Learning Models Using combo Library.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

PyOD: A Python Toolbox for Scalable Outlier Detection.

[BibT_eX]

[DOI]

Zain Nasrullah

J. Mach. Learn. Res., 2019

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles.

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

Colin Wan

CoRR, 2019

LSCP: Locally Selective Combination in Parallel Outlier Ensembles.

[BibT_eX]

[DOI]

Zain Nasrullah

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Music Artist Classification with Convolutional Recurrent Neural Networks.

[BibT_eX]

[DOI]

Zain Nasrullah

Proceedings of the International Joint Conference on Neural Networks, 2019

2018

Employee Turnover Prediction with Machine Learning: A Reliable Approach.

[BibT_eX]

[DOI]

Francesca Cheng

Boyang Fu

Xiaoyu Zhu

Proceedings of the Intelligent Systems and Applications, 2018

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning.

[BibT_eX]

[DOI]