Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

21 February 2018

Papers citing "Learning to Explain: An Information-Theoretic Perspective on Model Interpretation"

50 / 52 papers shown

Title
Axiomatic Explainer Globalness via Optimal Transport Davin Hill Josh Bone A. Masoomi Max Torop Jennifer Dy 93 1 0 13 Mar 2025
A Neural Difference-of-Entropies Estimator for Mutual Information Haoran Ni Martin Lotz SSL DRL 87 0 0 18 Feb 2025
Building Bridges, Not Walls -- Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution Shichang Zhang Tessa Han Usha Bhalla Hima Lakkaraju FAtt 145 0 0 17 Feb 2025
Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector Xianren Zhang Dongwon Lee Suhang Wang VLM FAtt 37 3 0 27 Jul 2024
Feature Inference Attack on Shapley Values Xinjian Luo Yangfan Jiang X. Xiao AAML FAtt 19 19 0 16 Jul 2024
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models Jayneel Parekh Quentin Bouniot Pavlo Mozharovskyi A. Newson Florence dÁlché-Buc SSL 53 1 0 01 Jul 2024
Efficient and Accurate Explanation Estimation with Distribution Compression Hubert Baniecki Giuseppe Casalicchio Bernd Bischl Przemyslaw Biecek FAtt 36 3 0 26 Jun 2024
Partial Information Decomposition for Data Interpretability and Feature Selection Charles Westphal Stephen Hailes Mirco Musolesi 32 0 0 29 May 2024
Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text Yuyu Jia Qing Zhou Wei Huang Junyu Gao Qi. Wang VLM 22 1 0 21 May 2024
Multiple Different Black Box Explanations for Image Classifiers Hana Chockler D. A. Kelly Daniel Kroening FAtt 11 0 0 25 Sep 2023
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods Robin Hesse Simone Schaub-Meyer Stefan Roth AAML 27 32 0 11 Aug 2023
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability Usha Bhalla Suraj Srinivas Himabindu Lakkaraju FAtt CML 13 6 0 27 Jul 2023
Efficient Learning of Discrete-Continuous Computation Graphs David Friede Mathias Niepert 8 3 0 26 Jul 2023
BELLA: Black box model Explanations by Local Linear Approximations N. Radulovic Albert Bifet Fabian M. Suchanek FAtt 22 1 0 18 May 2023
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion Kamil Adamczewski Christos Sakaridis Vaishakh Patil Luc Van Gool 23 1 0 21 Mar 2023
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation N. Jethani A. Saporta Rajesh Ranganath FAtt 29 10 0 24 Feb 2023
Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations Zhenxiao Cheng Jie Zhou Wen Wu Qin Chen Liang He 16 3 0 21 Feb 2023
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators Yuan-Chia Cheng Zu-Yun Shiau Fu-En Yang Yu-Chiang Frank Wang 31 2 0 19 Feb 2023
Personalized Interpretable Classification Zengyou He Yifan Tang Yifan Tang Lianyu Hu Yan Liu Yan Liu 15 0 0 06 Feb 2023
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations Angelos Chatzimparmpas R. Martins I. Jusufi K. Kucher Fabrice Rossi A. Kerren FAtt 15 160 0 22 Dec 2022
On the Explainability of Natural Language Processing Deep Models Julia El Zini M. Awad 25 81 0 13 Oct 2022
Sequential Attention for Feature Selection T. Yasuda M. Bateni Lin Chen Matthew Fahrbach Gang Fu Vahab Mirrokni 15 11 0 29 Sep 2022
L2XGNN: Learning to Explain Graph Neural Networks G. Serra Mathias Niepert 26 7 0 28 Sep 2022
A novel evaluation methodology for supervised Feature Ranking algorithms Jeroen G. S. Overschie 11 0 0 09 Jul 2022
Learning to Increase the Power of Conditional Randomization Tests Shalev Shaer Yaniv Romano CML 8 2 0 03 Jul 2022
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models Liu Zhendong Wenyu Jiang Yan Zhang Chongjun Wang CML 6 0 0 22 Jun 2022
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning Sihang Li Xiang Wang An Zhang Y. Wu Xiangnan He Tat-Seng Chua 11 93 0 16 Jun 2022
A Functional Information Perspective on Model Interpretation Itai Gat Nitay Calderon Roi Reichart Tamir Hazan AAML FAtt 28 6 0 12 Jun 2022
OmniXAI: A Library for Explainable AI Wenzhuo Yang Hung Le Tanmay Laud Silvio Savarese S. Hoi SyDa 8 38 0 01 Jun 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 41 98 0 16 May 2022
Causality Inspired Representation Learning for Domain Generalization Fangrui Lv Jian Liang Shuang Li Bin Zang Chi Harold Liu Ziteng Wang Di Liu CML OOD 20 158 0 27 Mar 2022
SOInter: A Novel Deep Energy Based Interpretation Method for Explaining Structured Output Models S. F. Seyyedsalehi Mahdieh Soleymani Hamid R. Rabiee 15 0 0 20 Feb 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 172 183 0 03 Feb 2022
Making a (Counterfactual) Difference One Rationale at a Time Michael J. Plyler Michal Green Min Chi 16 10 0 13 Jan 2022
Efficient Decompositional Rule Extraction for Deep Neural Networks Mateo Espinosa Zarlenga Z. Shams M. Jamnik 14 16 0 24 Nov 2021
Understanding Interlocking Dynamics of Cooperative Rationalization Mo Yu Yang Zhang Shiyu Chang Tommi Jaakkola 14 41 0 26 Oct 2021
Counterfactual Explanations for Neural Recommenders Khanh Tran Azin Ghazimatin Rishiraj Saha Roy AAML CML 44 65 0 11 May 2021
Towards Rigorous Interpretations: a Formalisation of Feature Attribution Darius Afchar Romain Hennequin Vincent Guigue FAtt 24 20 0 26 Apr 2021
Explanations for Occluded Images Hana Chockler Daniel Kroening Youcheng Sun 13 21 0 05 Mar 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 76 70 0 02 Mar 2021
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 22 239 0 21 Nov 2020
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang FAtt 16 17 0 14 Oct 2020
When is invariance useful in an Out-of-Distribution Generalization problem ? Masanori Koyama Shoichiro Yamaguchi OOD 20 64 0 04 Aug 2020
Gradient Estimation with Stochastic Softmax Tricks Max B. Paulus Dami Choi Daniel Tarlow Andreas Krause Chris J. Maddison BDL 19 85 0 15 Jun 2020
Importance-Driven Deep Learning System Testing Simos Gerasimou Hasan Ferit Eniser A. Sen Alper Çakan AAML VLM 19 97 0 09 Feb 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 26 343 0 17 Jan 2020
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 29 203 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 11 59 0 04 Oct 2019
Explaining and Interpreting LSTMs L. Arras Jose A. Arjona-Medina Michael Widrich G. Montavon Michael Gillhofer K. Müller Sepp Hochreiter Wojciech Samek FAtt AI4TS 10 78 0 25 Sep 2019
Multimodal Explanations by Predicting Counterfactuality in Videos Atsushi Kanehira Kentaro Takemoto S. Inayoshi Tatsuya Harada 18 35 0 04 Dec 2018