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"

22 / 22 papers shown

Title
Deeper Understanding of Black-box Predictions via Generalized Influence Functions Hyeonsu Lyu Jonggyu Jang Sehyun Ryu H. Yang TDI AI4CE 20 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 34 217 0 03 Jun 2022
A Cheap Bootstrap Method for Fast Inference H. Lam 29 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 29 9 0 27 Oct 2021
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning Preethi Lahoti Krishna P. Gummadi Gerhard Weikum 34 3 0 09 Sep 2021
On the Importance of Regularisation & Auxiliary Information in OOD Detection John Mitros Brian Mac Namee 21 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 32 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 39 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 26 2 0 18 Mar 2021
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging Han Guo Nazneen Rajani Peter Hase Joey Tianyi Zhou Caiming Xiong TDI 41 102 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 26 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
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions Ahmed Alaa M. Schaar UD UQCV BDL TDI 16 53 0 29 Jun 2020
Influence Functions in Deep Learning Are Fragile S. Basu Phillip E. Pope S. Feizi TDI 37 219 0 25 Jun 2020
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets Zhihui Shao Jianyi Yang Shaolei Ren OODD 35 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 28 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 28 47 0 16 Mar 2020
The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services Yuantian Miao Minhui Xue Chao Chen Lei Pan Jinchao Zhang Benjamin Zi Hao Zhao Dali Kaafar Yang Xiang 11 34 0 17 May 2019
Tutorial: Safe and Reliable Machine Learning S. Saria Adarsh Subbaswamy FaML 30 82 0 15 Apr 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,675 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015