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Towards a consistent interpretation of AIOps models

Towards a consistent interpretation of AIOps models

4 February 2022
Yingzhe Lyu
Gopi Krishnan Rajbahadur
Dayi Lin
Boyuan Chen
Zhen Ming
Z. Jiang
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Towards a consistent interpretation of AIOps models"

15 / 15 papers shown
Title
A Closer Look at AUROC and AUPRC under Class Imbalance
A Closer Look at AUROC and AUPRC under Class Imbalance
Matthew B. A. McDermott
Lasse Hyldig Hansen
Haoran Zhang
Giovanni Angelotti
Jack Gallifant
145
39
0
11 Jan 2024
The impact of feature importance methods on the interpretation of defect
  classifiers
The impact of feature importance methods on the interpretation of defect classifiers
Gopi Krishnan Rajbahadur
Shaowei Wang
Yasutaka Kamei
Ahmed E. Hassan
FAtt
52
83
0
04 Feb 2022
Can We Trust Your Explanations? Sanity Checks for Interpreters in
  Android Malware Analysis
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis
Ming Fan
Wenying Wei
Xiaofei Xie
Yang Liu
X. Guan
Ting Liu
FAttAAML
99
38
0
13 Aug 2020
Improving Reproducibility in Machine Learning Research (A Report from
  the NeurIPS 2019 Reproducibility Program)
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Joelle Pineau
Philippe Vincent-Lamarre
Koustuv Sinha
V. Larivière
A. Beygelzimer
Florence dÁlché-Buc
E. Fox
Hugo Larochelle
103
361
0
27 Mar 2020
DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud
  Services
DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services
Chetan Bansal
Sundararajan Renganathan
Ashima Asudani
Olivier Midy
Mathru Janakiraman
53
48
0
11 Oct 2019
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods
Oana-Maria Camburu
Eleonora Giunchiglia
Jakob N. Foerster
Thomas Lukasiewicz
Phil Blunsom
FAttAAML
94
61
0
04 Oct 2019
Evaluating Explanation Methods for Deep Learning in Security
Evaluating Explanation Methods for Deep Learning in Security
Alexander Warnecke
Dan Arp
Christian Wressnegger
Konrad Rieck
XAIAAMLFAtt
62
94
0
05 Jun 2019
On the (In)fidelity and Sensitivity for Explanations
On the (In)fidelity and Sensitivity for Explanations
Chih-Kuan Yeh
Cheng-Yu Hsieh
A. Suggala
David I. Inouye
Pradeep Ravikumar
FAtt
98
455
0
27 Jan 2019
Sanity Checks for Saliency Maps
Sanity Checks for Saliency Maps
Julius Adebayo
Justin Gilmer
M. Muelly
Ian Goodfellow
Moritz Hardt
Been Kim
FAttAAMLXAI
152
1,972
0
08 Oct 2018
Model Cards for Model Reporting
Model Cards for Model Reporting
Margaret Mitchell
Simone Wu
Andrew Zaldivar
Parker Barnes
Lucy Vasserman
Ben Hutchinson
Elena Spitzer
Inioluwa Deborah Raji
Timnit Gebru
144
1,910
0
05 Oct 2018
Datasheets for Datasets
Datasheets for Datasets
Timnit Gebru
Jamie Morgenstern
Briana Vecchione
Jennifer Wortman Vaughan
Hanna M. Wallach
Hal Daumé
Kate Crawford
294
2,201
0
23 Mar 2018
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
22,090
0
22 May 2017
SLA Violation Prediction In Cloud Computing: A Machine Learning
  Perspective
SLA Violation Prediction In Cloud Computing: A Machine Learning Perspective
Reyhane Askari Hemmat
A. Hafid
39
24
0
30 Nov 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.2K
17,071
0
16 Feb 2016
Unachievable Region in Precision-Recall Space and Its Effect on
  Empirical Evaluation
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation
Kendrick Boyd
Vítor Santos Costa
Jesse Davis
David Page
UQCV
115
105
0
18 Jun 2012
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