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 / 302 papers shown

Title
What went wrong and when? Instance-wise Feature Importance for Time-series Models S. Tonekaboni Shalmali Joshi Kieran Campbell David Duvenaud Anna Goldenberg FAtt OOD AI4TS 53 14 0 05 Mar 2020
An Information-Theoretic Approach to Personalized Explainable Machine Learning A. Jung P. H. Nardelli 6 20 0 01 Mar 2020
Importance-Driven Deep Learning System Testing Simos Gerasimou Hasan Ferit Eniser A. Sen Alper Çakan AAML VLM 32 98 0 09 Feb 2020
DANCE: Enhancing saliency maps using decoys Y. Lu Wenbo Guo Masashi Sugiyama William Stafford Noble AAML 40 14 0 03 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 37 345 0 17 Jan 2020
Clusters in Explanation Space: Inferring disease subtypes from model explanations Marc-Andre Schulz M. Chapman-Rounds Manisha Verma D. Bzdok K. Georgatzis 12 2 0 18 Dec 2019
Automated Dependence Plots David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar 12 1 0 02 Dec 2019
EMAP: Explanation by Minimal Adversarial Perturbation M. Chapman-Rounds Marc-Andre Schulz Erik Pazos K. Georgatzis AAML FAtt 10 6 0 02 Dec 2019
DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning Samet Demir Hasan Ferit Eniser A. Sen AAML 11 28 0 24 Nov 2019
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control Mo Yu Shiyu Chang Yang Zhang Tommi Jaakkola 21 140 0 29 Oct 2019
A Game Theoretic Approach to Class-wise Selective Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 22 60 0 28 Oct 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 40 205 0 27 Oct 2019
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye C. Rowat Ilya Feige 16 180 0 14 Oct 2019
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations Oana-Maria Camburu Brendan Shillingford Pasquale Minervini Thomas Lukasiewicz Phil Blunsom AAML GAN 13 96 0 07 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 29 60 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 21 79 0 25 Sep 2019
How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility? Akhil Gupta Naman Shukla Lavanya Marla A. Kolbeinsson Kartik Yellepeddi 6 39 0 24 Sep 2019
BPMR: Bayesian Probabilistic Multivariate Ranking Nan Wang Hongning Wang 23 0 0 18 Sep 2019
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation Hanjie Chen Yangfeng Ji 16 9 0 10 Sep 2019
TabNet: Attentive Interpretable Tabular Learning Sercan Ö. Arik Tomas Pfister LMTD 55 1,289 0 20 Aug 2019
Neural Image Compression and Explanation Xiang Li Shihao Ji 12 10 0 09 Aug 2019
How model accuracy and explanation fidelity influence user trust A. Papenmeier G. Englebienne C. Seifert FaML 20 108 0 26 Jul 2019
Explaining an increase in predicted risk for clinical alerts Michaela Hardt A. Rajkomar Gerardo Flores Andrew M. Dai M. Howell Greg S. Corrado Claire Cui Moritz Hardt FAtt 17 12 0 10 Jul 2019
ASAC: Active Sensing using Actor-Critic models Jinsung Yoon James Jordon M. Schaar CML 11 16 0 16 Jun 2019
Deep-gKnock: nonlinear group-feature selection with deep neural network G. Zhu Tingting Zhao 28 13 0 24 May 2019
Evaluating Recurrent Neural Network Explanations L. Arras Ahmed Osman K. Müller Wojciech Samek XAI FAtt 24 88 0 26 Apr 2019
Interpreting Black Box Models via Hypothesis Testing Collin Burns Jesse Thomason Wesley Tansey FAtt 11 9 0 29 Mar 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 37 1,289 0 10 Mar 2019
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features Pasha Khosravi Yitao Liang YooJung Choi Mathias Niepert 14 44 0 05 Mar 2019
Explaining a black-box using Deep Variational Information Bottleneck Approach Seo-Jin Bang P. Xie Heewook Lee Wei Wu Eric Xing XAI FAtt 22 75 0 19 Feb 2019
F-BLEAU: Fast Black-box Leakage Estimation Giovanni Cherubin K. Chatzikokolakis C. Palamidessi 23 34 0 04 Feb 2019
Reparameterizable Subset Sampling via Continuous Relaxations Sang Michael Xie Stefano Ermon BDL 11 96 0 29 Jan 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 39 449 0 27 Jan 2019
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction Abubakar Abid M. F. Balin James Zou SyDa 23 224 0 27 Jan 2019
On Network Science and Mutual Information for Explaining Deep Neural Networks Brian Davis Umang Bhatt Kartikeya Bhardwaj R. Marculescu J. M. F. Moura FedML SSL FAtt 21 10 0 20 Jan 2019
Ten ways to fool the masses with machine learning F. Minhas Amina Asif Asa Ben-Hur FedML HAI 33 5 0 07 Jan 2019
Explaining Neural Networks Semantically and Quantitatively Runjin Chen Hao Chen Ge Huang Jie Ren Quanshi Zhang FAtt 23 54 0 18 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan Marta Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 24 50 0 18 Dec 2018
Learning to Explain with Complemental Examples Atsushi Kanehira Tatsuya Harada 12 40 0 04 Dec 2018
Multimodal Explanations by Predicting Counterfactuality in Videos Atsushi Kanehira Kentaro Takemoto S. Inayoshi Tatsuya Harada 26 35 0 04 Dec 2018
YASENN: Explaining Neural Networks via Partitioning Activation Sequences Yaroslav Zharov Denis Korzhenkov J. Lyu Alexander Tuzhilin FAtt AAML 11 6 0 07 Nov 2018
Feature Selection using Stochastic Gates Yutaro Yamada Ofir Lindenbaum S. Negahban Y. Kluger 22 43 0 09 Oct 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 37 77 0 09 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 67 1,931 0 08 Oct 2018
Rule induction for global explanation of trained models Madhumita Sushil Simon Suster Walter Daelemans FAtt 14 17 0 29 Aug 2018
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan FAtt TDI 14 213 0 08 Aug 2018
Computationally Efficient Measures of Internal Neuron Importance Avanti Shrikumar Jocelin Su A. Kundaje FAtt 18 29 0 26 Jul 2018
Explaining Image Classifiers by Counterfactual Generation C. Chang Elliot Creager Anna Goldenberg David Duvenaud VLM 11 265 0 20 Jul 2018
Optimal Piecewise Local-Linear Approximations Kartik Ahuja W. Zame M. Schaar FAtt 27 1 0 27 Jun 2018
Information Constraints on Auto-Encoding Variational Bayes Romain Lopez Jeffrey Regier Michael I. Jordan N. Yosef BDL 14 123 0 22 May 2018