Generative causal explanations of black-box classifiers

24 June 2020

Matthew R. O’Shaughnessy

Papers citing "Generative causal explanations of black-box classifiers"

41 / 41 papers shown

Title
Explainable Artificial Intelligence for Medical Applications: A Review Qiyang Sun Alican Akman Björn Schuller 81 6 0 15 Nov 2024
Linking Model Intervention to Causal Interpretation in Model Explanation Debo Cheng Ziqi Xu Jiuyong Li Lin Liu Kui Yu T. Le Jixue Liu CML 23 0 0 21 Oct 2024
Structural Causality-based Generalizable Concept Discovery Models Sanchit Sinha Guangzhi Xiong Aidong Zhang CML DRL 26 0 0 20 Oct 2024
CoLiDR: Concept Learning using Aggregated Disentangled Representations Sanchit Sinha Guangzhi Xiong Aidong Zhang 29 0 0 27 Jul 2024
Benchmarking the Attribution Quality of Vision Models Robin Hesse Simone Schaub-Meyer Stefan Roth FAtt 31 3 0 16 Jul 2024
A survey on Concept-based Approaches For Model Improvement Avani Gupta P. J. Narayanan LRM 29 5 0 21 Mar 2024
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation Ricardo Moreira Jacopo Bono Mário Cardoso Pedro Saleiro Mário A. T. Figueiredo P. Bizarro CML 28 4 0 16 Jan 2024
Causal State Distillation for Explainable Reinforcement Learning Wenhao Lu Xufeng Zhao Thilo Fryen Jae Hee Lee Mengdi Li S. Magg Stefan Wermter CML 41 2 0 30 Dec 2023
SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation Bo Pan Zhenke Liu Yifei Zhang Liang Zhao 27 2 0 11 Oct 2023
Learning to Receive Help: Intervention-Aware Concept Embedding Models M. Zarlenga Katherine M. Collins Krishnamurthy Dvijotham Adrian Weller Z. Shams M. Jamnik 19 23 0 29 Sep 2023
Towards LLM-guided Causal Explainability for Black-box Text Classifiers Amrita Bhattacharjee Raha Moraffah Joshua Garland Huan Liu 23 33 0 23 Sep 2023
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods Robin Hesse Simone Schaub-Meyer Stefan Roth AAML 34 32 0 11 Aug 2023
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs Kaiwen Xu Kazuto Fukuchi Youhei Akimoto Jun Sakuma 21 2 0 27 May 2023
$On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective$ On Pitfalls of $\textit{RemOve-And-Retrain}$ : Data Processing Inequality Perspective J. Song Keumgang Cha Junghoon Seo 34 2 0 26 Apr 2023
Towards Learning and Explaining Indirect Causal Effects in Neural Networks Abbaavaram Gowtham Reddy Saketh Bachu Harsh Nilesh Pathak Ben Godfrey V. Balasubramanian V. Varshaneya Satya Narayanan Kar CML 26 0 0 24 Mar 2023
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions Julia El Zini Mohamad Mansour M. Awad 19 1 0 19 Jan 2023
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis Kaizhong Zheng Shujian Yu Badong Chen CML 25 31 0 04 Jan 2023
Data-Centric Debugging: mitigating model failures via targeted data collection Sahil Singla Atoosa Malemir Chegini Mazda Moayeri Soheil Feiz 14 4 0 17 Nov 2022
GLANCE: Global to Local Architecture-Neutral Concept-based Explanations Avinash Kori Ben Glocker Francesca Toni 24 6 0 05 Jul 2022
Explanatory causal effects for model agnostic explanations Jiuyong Li Ha Xuan Tran T. Le Lin Liu Kui Yu Jixue Liu CML 22 1 0 23 Jun 2022
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces Kamran Alipour Aditya Lahiri Ehsan Adeli Babak Salimi M. Pazzani CML 20 7 0 10 Jun 2022
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning Yikuan Li M. Mamouei Shishir Rao A. Hassaine D. Canoy Thomas Lukasiewicz K. Rahimi G. Salimi-Khorshidi OOD CML AI4CE 15 1 0 15 May 2022
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space Amil Dravid Florian Schiffers Boqing Gong Aggelos K. Katsaggelos GAN MedIm 25 9 0 11 Apr 2022
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks Wanyu Lin Hao Lan Hao Wang Baochun Li BDL CML 31 50 0 29 Mar 2022
Core Risk Minimization using Salient ImageNet Sahil Singla Mazda Moayeri S. Feizi 25 14 0 28 Mar 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 23 41 0 15 Feb 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
On Causally Disentangled Representations Abbavaram Gowtham Reddy Benin Godfrey L V. Balasubramanian OOD CML 28 21 0 10 Dec 2021
Salient ImageNet: How to discover spurious features in Deep Learning? Sahil Singla S. Feizi AAML VLM 14 114 0 08 Oct 2021
Cartoon Explanations of Image Classifiers Stefan Kolek Duc Anh Nguyen Ron Levie Joan Bruna Gitta Kutyniok FAtt 35 14 0 07 Oct 2021
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation Thien Q. Tran Kazuto Fukuchi Youhei Akimoto Jun Sakuma CML 37 10 0 09 Sep 2021
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs Y. Poels Vlado Menkovski CoGe DRL 11 3 0 20 Aug 2021
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey Richard Dazeley Peter Vamplew Francisco Cruz 24 59 0 20 Aug 2021
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms Martin Pawelczyk Sascha Bielawski J. V. D. Heuvel Tobias Richter Gjergji Kasneci CML 11 105 0 02 Aug 2021
Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang Yossi Gandelsman Michal Yarom Yoav Wald G. Elidan ... William T. Freeman Phillip Isola Amir Globerson Michal Irani Inbar Mosseri GAN 35 152 0 27 Apr 2021
Instance-wise Causal Feature Selection for Model Interpretation Pranoy Panda Sai Srinivas Kancheti V. Balasubramanian CML 47 16 0 26 Apr 2021
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models Dong Wang Yuewei Yang Chenyang Tao Zhe Gan Liqun Chen Fanjie Kong Ricardo Henao Lawrence Carin 24 0 0 06 Dec 2020
Understanding Failures of Deep Networks via Robust Feature Extraction Sahil Singla Besmira Nushi S. Shah Ece Kamar Eric Horvitz FAtt 13 82 0 03 Dec 2020
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 13 776 0 09 Jul 2020
Explaining Visual Models by Causal Attribution Álvaro Parafita Jordi Vitrià CML FAtt 62 35 0 19 Sep 2019
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 227 201 0 06 Jul 2017