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1901.00403
Cited By
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning
2 January 2019
Peter F. Schulam
S. Saria
OOD
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ArXiv
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Papers citing
"Can You Trust This Prediction? Auditing Pointwise Reliability After Learning"
17 / 17 papers shown
Title
Deeper Understanding of Black-box Predictions via Generalized Influence Functions
Hyeonsu Lyu
Jonggyu Jang
Sehyun Ryu
H. Yang
TDI
AI4CE
13
5
0
09 Dec 2023
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein
Annika Reinke
Patrick Godau
M. Tizabi
Florian Buettner
...
Aleksei Tiulpin
Sotirios A. Tsaftaris
Ben Van Calster
Gaël Varoquaux
Paul F. Jäger
22
214
0
03 Jun 2022
A Cheap Bootstrap Method for Fast Inference
H. Lam
14
11
0
31 Jan 2022
Algorithmic encoding of protected characteristics in image-based models for disease detection
Ben Glocker
Charles Jones
Mélanie Bernhardt
S. Winzeck
21
9
0
27 Oct 2021
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning
Preethi Lahoti
Krishna P. Gummadi
G. Weikum
26
3
0
09 Sep 2021
On the Importance of Regularisation & Auxiliary Information in OOD Detection
John Mitros
Brian Mac Namee
11
2
0
15 Jul 2021
Test for non-negligible adverse shifts
Vathy M. Kamulete
13
3
0
07 Jul 2021
Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases
Md. Abdullah Al Alamin
Gias Uddin
24
11
0
03 May 2021
Influence Based Defense Against Data Poisoning Attacks in Online Learning
Sanjay Seetharaman
Shubham Malaviya
KV Rosni
Manish Shukla
S. Lodha
TDI
AAML
26
9
0
24 Apr 2021
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
E. Chen
Andy Kim
R. Krishnan
J. Long
A. Ng
Pranav Rajpurkar
19
2
0
18 Mar 2021
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings
John Mitros
A. Pakrashi
Brian Mac Namee
UQCV
11
2
0
03 Sep 2020
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Zhe Chen
Yuyan Wang
Dong Lin
D. Cheng
Lichan Hong
Ed H. Chi
Claire Cui
28
16
0
17 Aug 2020
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets
Zhihui Shao
Jianyi Yang
Shaolei Ren
OODD
27
11
0
16 Jun 2020
Anomalous Example Detection in Deep Learning: A Survey
Saikiran Bulusu
B. Kailkhura
Bo-wen Li
P. Varshney
D. Song
AAML
13
47
0
16 Mar 2020
Tutorial: Safe and Reliable Machine Learning
S. Saria
Adarsh Subbaswamy
FaML
23
82
0
15 Apr 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
270
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
252
9,134
0
06 Jun 2015
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