Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions

20 July 2021

Papers citing "Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions"

10 / 10 papers shown

Title
Explaining Black-Box Models through Counterfactuals Patrick Altmeyer A. V. Deursen Cynthia C. S. Liem CML LRM 37 2 0 14 Aug 2023
Explaining Model Confidence Using Counterfactuals Thao Le Tim Miller Ronal Singh L. Sonenberg 19 4 0 10 Mar 2023
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning Jasmina Gajcin Ivana Dusparic CML 32 3 0 08 Mar 2023
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ Eoin Delaney A. Pakrashi Derek Greene Markt. Keane 35 16 0 16 Dec 2022
Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare Marissa Radensky Dustin Burson Rajya Bhaiya Daniel S. Weld 26 0 0 27 Apr 2022
A Rationale-Centric Framework for Human-in-the-loop Machine Learning Jinghui Lu Linyi Yang Brian Mac Namee Yue Zhang 27 39 0 24 Mar 2022
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 26 162 0 20 Oct 2020
BRPO: Batch Residual Policy Optimization Kentaro Kanamori Yinlam Chow Takuya Takagi Hiroki Arimura Honglak Lee Ken Kobayashi Craig Boutilier OffRL 141 46 0 08 Feb 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,683 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015