ICML 2022spotlight12 citations
FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers
Abishek Sankararaman, Balakrishnan Narayanaswamy, Vikramank Y Singh, Zhao Song
Abstract
Technology improvements have made it easier than ever to collect diverse telemetry at high resolution from any cyber or physical system, for both monitoring and control. In the domain of monitoring, anomaly detection has become an important problem in many research areas ranging from IoT and sensor networks to devOps. These systems operate in real, noisy and non-stationary environments. A fundamental question is then, ‘
BibTeX
@InProceedings{pmlr-v162-sankararaman22a,
title = {{FITNESS}: ({F}ine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers},
author = {Sankararaman, Abishek and Narayanaswamy, Balakrishnan and Singh, Vikramank Y and Song, Zhao},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {19153--19177},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/sankararaman22a/sankararaman22a.pdf},
url = {https://proceedings.mlr.press/v162/sankararaman22a.html},
abstract = {Technology improvements have made it easier than ever to collect diverse telemetry at high resolution from any cyber or physical system, for both monitoring and control. In the domain of monitoring, anomaly detection has become an important problem in many research areas ranging from IoT and sensor networks to devOps. These systems operate in real, noisy and non-stationary environments. A fundamental question is then, ‘