Autood: Automatic outlier detection

L Cao, Y Yan, Y Wang, S Madden… - Proceedings of the ACM …, 2023 - dl.acm.org
… effectiveness of AutoOD using a variety of benchmark outlier detection data sets [17… AutoOD
that uses a set of unsupervised outlier detectors to automatically produce high quality outliers

Autood: Automated outlier detection via curiosity-guided search and self-imitation learning

Y Li, Z Chen, D Zha, K Zhou, H Jin, H Chen… - arXiv preprint arXiv …, 2020 - arxiv.org
detection and semantic segmentation, contemporary NAS methods are not suitable for outlier
detection … paper, we propose AutoOD, an automated outlier detection framework, which …

Autood: Neural architecture search for outlier detection

Y Li, Z Chen, D Zha, K Zhou, H Jin… - 2021 IEEE 37th …, 2021 - ieeexplore.ieee.org
detection and semantic segmentation, contemporary NAS methods are not suitable for outlier
detection … paper, we propose AutoOD, an automated outlier detection framework, which …

A demonstration of autood: a self-tuning anomaly detection system

D Hofmann, P VanNostrand, H Zhang, Y Yan… - Proceedings of the …, 2022 - dl.acm.org
… Using these automatically produced labels, AutoOD then trains a supervised classification
model. The latter then is applied to the remaining (unlabeled) objects to infer their status, …

Automatic unsupervised outlier model selection

Y Zhao, R Rossi, L Akoglu - Advances in Neural …, 2021 - proceedings.neurips.cc
outlier detection task on a new dataset, how can we automatically select a good outlier detection
… Recent work include AutoOD [29] that focuses on automatic neural architecture search, …

Tods: An automated time series outlier detection system

KH Lai, D Zha, G Wang, J Xu, Y Zhao… - Proceedings of the aaai …, 2021 - ojs.aaai.org
… for automated time series outlier detection. We build TODS upon our previous research and
open-source efforts in automated machine learning and outlier detection, hoping that it can …

[PDF][PDF] AutoOD 2.0: Elevating User Experience and Multi-User Functionality

T Andrews - 2024 - digital.wpi.edu
… This paper describes the changes implemented to AutoOD, an automated outlier detection
… to run AutoOD multiple times, access and download logs, set custom outlier detection

AutoML: state of the art with a focus on anomaly detection, challenges, and research directions

M Bahri, F Salutari, A Putina, M Sozio - International Journal of Data …, 2022 - Springer
… proposed an Automated Outlier Detection (AutoOD) framework that aims to find the optimal
neural network model within a predefined search space for unsupervised outlier detection. It …

[PDF][PDF] Instance-level metalearning for outlier detection

L Vu, P Kirchner, CC Aggarwal… - … Joint Conference on …, 2024 - ijcai.org
automatedAutoOD show impressive performance on a wide variety of data sets. In future
work, we will examine how to speed up T-AutoOD by leveraging the intuition that some outliers

[PDF][PDF] Systematic evaluation of cash search strategies for unsupervised anomaly detection

I Antoniadis, V Vercruyssen… - … Workshop on Learning …, 2022 - proceedings.mlr.press
AutoOD to tackle the neural architecture search problem for unsupervised anomaly detection
… end-to-end tool for automatic construction of optimal outlier detection pipelines. The search …