Can You Trust This Prediction? Auditing Pointwise Reliability After Learning

2 January 2019

Papers citing "Can You Trust This Prediction? Auditing Pointwise Reliability After Learning"

20 / 20 papers shown

Title
Deeper Understanding of Black-box Predictions via Generalized Influence Functions Hyeonsu Lyu Jonggyu Jang Sehyun Ryu H. Yang TDI AI4CE 18 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 13 2 0 15 Jul 2021
Test for non-negligible adverse shifts Vathy M. Kamulete 15 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 28 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 21 2 0 18 Mar 2021
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging Han Guo Nazneen Rajani Peter Hase Mohit Bansal Caiming Xiong TDI 19 102 0 31 Dec 2020
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
Influence Functions in Deep Learning Are Fragile S. Basu Phillip E. Pope S. Feizi TDI 6 219 0 25 Jun 2020
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets Zhihui Shao Jianyi Yang Shaolei Ren OODD 27 11 0 16 Jun 2020
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure Koorosh Aslansefat Ioannis Sorokos D. Whiting Ramin Tavakoli Kolagari Y. Papadopoulos 10 34 0 27 May 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 261 9,134 0 06 Jun 2015