Robust and Stable Black Box Explanations

12 November 2020

Himabindu Lakkaraju

Papers citing "Robust and Stable Black Box Explanations"

47 / 47 papers shown

Title
Axiomatic Explainer Globalness via Optimal Transport Davin Hill Josh Bone A. Masoomi Max Torop Jennifer Dy 100 1 0 13 Mar 2025
Interpretable Model Drift Detection Pranoy Panda Kancheti Sai Srinivas V. Balasubramanian Gaurav Sinha 70 0 0 09 Mar 2025
An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated Text Loris Schoenegger Yuxi Xia Benjamin Roth FAtt 38 0 0 26 Aug 2024
On the Robustness of Global Feature Effect Explanations Hubert Baniecki Giuseppe Casalicchio Bernd Bischl Przemyslaw Biecek 23 2 0 13 Jun 2024
Robust Explainable Recommendation Sairamvinay Vijayaraghavan Prasant Mohapatra AAML 23 0 0 03 May 2024
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 56 2 0 25 Apr 2024
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors Md Abdul Kadir G. Addluri Daniel Sonntag AAML 44 0 0 25 Mar 2024
X Hacking: The Threat of Misguided AutoML Rahul Sharma Sergey Redyuk Sumantrak Mukherjee Andrea Sipka Sebastian Vollmer David Selby 26 2 0 16 Jan 2024
Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks Tanmay Garg Deepika Vemuri Vineeth N. Balasubramanian GAN 11 2 0 09 Jan 2024
Rethinking Robustness of Model Attributions Sandesh Kamath Sankalp Mittal Amit Deshpande Vineeth N. Balasubramanian 20 2 0 16 Dec 2023
How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors? Zachariah Carmichael Walter J. Scheirer FAtt 36 4 0 27 Oct 2023
Confident Feature Ranking Bitya Neuhof Y. Benjamini FAtt 19 3 0 28 Jul 2023
Explainable AI using expressive Boolean formulas G. Rosenberg J. K. Brubaker M. Schuetz Grant Salton Zhihuai Zhu E. Zhu Serdar Kadioğlu S. E. Borujeni H. Katzgraber 25 8 0 06 Jun 2023
Adversarial attacks and defenses in explainable artificial intelligence: A survey Hubert Baniecki P. Biecek AAML 42 63 0 06 Jun 2023
Post Hoc Explanations of Language Models Can Improve Language Models Satyapriya Krishna Jiaqi Ma Dylan Slack Asma Ghandeharioun Sameer Singh Himabindu Lakkaraju ReLM LRM 18 53 0 19 May 2023
Robust Explanation Constraints for Neural Networks Matthew Wicker Juyeon Heo Luca Costabello Adrian Weller FAtt 21 17 0 16 Dec 2022
Understanding and Enhancing Robustness of Concept-based Models Sanchit Sinha Mengdi Huai Jianhui Sun Aidong Zhang AAML 25 18 0 29 Nov 2022
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities Andrea Tocchetti Lorenzo Corti Agathe Balayn Mireia Yurrita Philip Lippmann Marco Brambilla Jie-jin Yang 19 10 0 17 Oct 2022
Inferring Sensitive Attributes from Model Explanations Vasisht Duddu A. Boutet MIACV SILM 17 16 0 21 Aug 2022
A Query-Optimal Algorithm for Finding Counterfactuals Guy Blanc Caleb M. Koch Jane Lange Li-Yang Tan 8 4 0 14 Jul 2022
Explaining the root causes of unit-level changes Kailash Budhathoki George Michailidis Dominik Janzing FAtt 23 4 0 26 Jun 2022
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions Zulqarnain Khan Davin Hill A. Masoomi Joshua Bone Jennifer Dy AAML 36 3 0 24 Jun 2022
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models Han Yuan Mingxuan Liu Lican Kang Chenkui Miao Ying Wu FAtt 11 8 0 24 Apr 2022
Framework for Evaluating Faithfulness of Local Explanations S. Dasgupta Nave Frost Michal Moshkovitz FAtt 111 61 0 01 Feb 2022
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning Amit Dhurandhar K. Ramamurthy Kartik Ahuja Vijay Arya FAtt 7 4 0 28 Jan 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 26 395 0 20 Jan 2022
Provably efficient, succinct, and precise explanations Guy Blanc Jane Lange Li-Yang Tan FAtt 24 35 0 01 Nov 2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations Saumitra Mishra Sanghamitra Dutta Jason Long Daniele Magazzeni AAML 9 58 0 30 Oct 2021
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools Davis Brown Henry Kvinge AAML 37 7 0 14 Oct 2021
Self-learn to Explain Siamese Networks Robustly Chao Chen Yifan Shen Guixiang Ma Xiangnan Kong S. Rangarajan Xi Zhang Sihong Xie 38 5 0 15 Sep 2021
A Framework for Learning Ante-hoc Explainable Models via Concepts Anirban Sarkar Deepak Vijaykeerthy Anindya Sarkar V. Balasubramanian LRM BDL 14 46 0 25 Aug 2021
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing Sanchit Sinha Hanjie Chen Arshdeep Sekhon Yangfeng Ji Yanjun Qi AAML FAtt 14 42 0 11 Aug 2021
Extending LIME for Business Process Automation Sohini Upadhyay Vatche Isahagian Vinod Muthusamy Yara Rizk FAtt 8 4 0 09 Aug 2021
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods Chirag Agarwal Marinka Zitnik Himabindu Lakkaraju 19 51 0 16 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 19 31 0 09 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das K. Kenthapadi AAML 11 21 0 08 Jun 2021
Evaluating Local Explanations using White-box Models Amir Hossein Akhavan Rahnama Judith Butepage Pierre Geurts Henrik Bostrom FAtt 17 0 0 04 Jun 2021
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay Shalmali Joshi Himabindu Lakkaraju 22 108 0 26 Feb 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 21 57 0 25 Feb 2021
Attribution Mask: Filtering Out Irrelevant Features By Recursively Focusing Attention on Inputs of DNNs Jaehwan Lee Joon‐Hyuk Chang TDI FAtt 19 0 0 15 Feb 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 25 22 0 14 Feb 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 15 62 0 18 Dec 2020
Learning Models for Actionable Recourse Alexis Ross Himabindu Lakkaraju Osbert Bastani FaML 29 19 0 12 Nov 2020
A Framework to Learn with Interpretation Jayneel Parekh Pavlo Mozharovskyi Florence dÁlché-Buc AI4CE FAtt 14 30 0 19 Oct 2020
Counterfactual Explanations for Machine Learning on Multivariate Time Series Data E. Ates Burak Aksar V. Leung A. Coskun AI4TS 48 65 0 25 Aug 2020
The best way to select features? Xin Man Ernest P. Chan 14 60 0 26 May 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017