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Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

11 August 2020

Himabindu Lakkaraju

Papers citing "Reliable Post hoc Explanations: Modeling Uncertainty in Explainability"

48 / 48 papers shown

Title
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models Célia Wafa Ayad Thomas Bonnier Benjamin Bosch Sonali Parbhoo Jesse Read FAtt XAI 139 0 0 11 Feb 2025
Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models Sepehr Kamahi Yadollah Yaghoobzadeh 89 0 0 21 Aug 2024
Efficient and Accurate Explanation Estimation with Distribution Compression Hubert Baniecki Giuseppe Casalicchio Bernd Bischl Przemyslaw Biecek FAtt 99 4 0 26 Jun 2024
Uncertainty Quantification for Gradient-based Explanations in Neural Networks Mihir Mulye Matias Valdenegro-Toro UQCV FAtt 95 0 0 25 Mar 2024
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 246 195 0 03 Feb 2022
Probabilistic Sufficient Explanations Eric Wang Pasha Khosravi Guy Van den Broeck XAI FAtt TPM 150 24 0 21 May 2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations Sushant Agarwal S. Jabbari Chirag Agarwal Sohini Upadhyay Zhiwei Steven Wu Himabindu Lakkaraju FAtt AAML 66 63 0 21 Feb 2021
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations Xingyu Zhao Wei Huang Xiaowei Huang Valentin Robu David Flynn FAtt 78 94 0 05 Dec 2020
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression Ian Covert Su-In Lee FAtt 56 171 0 02 Dec 2020
Gradient-based Analysis of NLP Models is Manipulable Junlin Wang Jens Tuyls Eric Wallace Sameer Singh AAML FAtt 65 58 0 12 Oct 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 91 31 0 16 Jun 2020
On Tractable Representations of Binary Neural Networks Weijia Shi Andy Shih Adnan Darwiche Arthur Choi TPM OffRL 41 69 0 05 Apr 2020
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 65 255 0 15 Nov 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 77 819 0 06 Nov 2019
bLIMEy: Surrogate Prediction Explanations Beyond LIME Kacper Sokol Alexander Hepburn Raúl Santos-Rodríguez Peter A. Flach FAtt 121 38 0 29 Oct 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 120 209 0 27 Oct 2019
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 70 61 0 04 Oct 2019
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Muhammad Rehman Zafar N. Khan FAtt 109 157 0 24 Jun 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 81 334 0 19 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla Soheil Feizi FAtt AAML 65 64 0 28 May 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 95 204 0 06 Feb 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 75 454 0 27 Jan 2019
Abduction-Based Explanations for Machine Learning Models Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt 57 226 0 26 Nov 2018
Explaining Deep Learning Models - A Bayesian Non-parametric Approach Wenbo Guo Sui Huang Yunzhe Tao Masashi Sugiyama Lin Lin BDL 40 47 0 07 Nov 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,970 0 08 Oct 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 121 550 0 18 Sep 2018
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan FAtt TDI 113 216 0 08 Aug 2018
Model Agnostic Supervised Local Explanations Gregory Plumb Denali Molitor Ameet Talwalkar FAtt LRM MILM 121 199 0 09 Jul 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 79 528 0 21 Jun 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers Andy Shih Arthur Choi Adnan Darwiche FAtt 74 243 0 09 May 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 91 698 0 21 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 115 2,360 0 01 Nov 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 133 867 0 29 Oct 2017
Interpretability via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 55 129 0 29 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 204 2,226 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,939 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 213 2,899 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 6,015 0 04 Mar 2017
Mapping chemical performance on molecular structures using locally interpretable explanations Leanne S. Whitmore Anthe George Corey M. Hudson FAtt 39 12 0 22 Nov 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 321 20,070 0 07 Oct 2016
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 86 838 0 16 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim Cynthia Rudin J. Shah 70 321 0 03 Mar 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,479 0 04 Sep 2014
Speeding up Convolutional Neural Networks with Low Rank Expansions Max Jaderberg Andrea Vedaldi Andrew Zisserman 130 1,465 0 15 May 2014
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L. Denton Wojciech Zaremba Joan Bruna Yann LeCun Rob Fergus FAtt 177 1,692 0 02 Apr 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,308 0 20 Dec 2013
Supersparse Linear Integer Models for Interpretable Classification Berk Ustun Stefano Tracà Cynthia Rudin 68 43 0 27 Jun 2013