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Leveraging Explanations in Interactive Machine Learning: An Overview

29 July 2022

Papers citing "Leveraging Explanations in Interactive Machine Learning: An Overview"

50 / 125 papers shown

Title
Debiasing Concept-based Explanations with Causal Analysis M. T. Bahadori David Heckerman FAtt CML 55 39 0 22 Jul 2020
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 50 97 0 20 Jul 2020
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 96 823 0 09 Jul 2020
Causality Learning: A New Perspective for Interpretable Machine Learning Guandong Xu Tri Dung Duong Q. Li S. Liu Xianzhi Wang XAI OOD CML 37 52 0 27 Jun 2020
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation L. Guan Mudit Verma Sihang Guo Ruohan Zhang Subbarao Kambhampati 89 42 0 26 Jun 2020
Influence Functions in Deep Learning Are Fragile S. Basu Phillip E. Pope Soheil Feizi TDI 125 235 0 25 Jun 2020
Cost-effective Interactive Attention Learning with Neural Attention Processes Jay Heo Junhyeong Park Hyewon Jeong Kwang Joon Kim Juho Lee Eunho Yang Sung Ju Hwang 36 8 0 09 Jun 2020
Interacting with Explanations through Critiquing Diego Antognini C. Musat Boi Faltings 28 22 0 22 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 103 378 0 30 Apr 2020
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 206 2,052 0 16 Apr 2020
On Interactive Machine Learning and the Potential of Cognitive Feedback C. J. Michael Dina M. Acklin J. Scheuerman 77 12 0 23 Mar 2020
Problems with Shapley-value-based explanations as feature importance measures Indra Elizabeth Kumar Suresh Venkatasubramanian C. Scheidegger Sorelle A. Friedler TDI FAtt 79 365 0 25 Feb 2020
Performative Prediction Juan C. Perdomo Tijana Zrnic Celestine Mendler-Dünner Moritz Hardt 134 319 0 16 Feb 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 51 340 0 14 Feb 2020
Concept Whitening for Interpretable Image Recognition Zhi Chen Yijie Bei Cynthia Rudin FAtt 73 320 0 05 Feb 2020
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency Kacper Sokol Peter A. Flach 37 176 0 27 Jan 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 101 212 0 15 Jan 2020
Explaining the Explainer: A First Theoretical Analysis of LIME Damien Garreau U. V. Luxburg FAtt 46 178 0 10 Jan 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 55 132 0 20 Dec 2019
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches Kacper Sokol Peter A. Flach XAI 90 302 0 11 Dec 2019
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge Laura Rieger Chandan Singh W. James Murdoch Bin Yu FAtt 83 215 0 30 Sep 2019
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data Divyansh Kaushik Eduard H. Hovy Zachary Chase Lipton CML 91 569 0 26 Sep 2019
Regional Tree Regularization for Interpretability in Black Box Models Mike Wu S. Parbhoo M. C. Hughes R. Kindle Leo Anthony Celi Maurizio Zazzi Volker Roth Finale Doshi-Velez 48 37 0 13 Aug 2019
Interpretable Image Recognition with Hierarchical Prototypes Peter Hase Chaofan Chen Oscar Li Cynthia Rudin VLM 83 111 0 25 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 81 332 0 19 Jun 2019
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning Artur Garcez Marco Gori Luís C. Lamb Luciano Serafini Michael Spranger Son N. Tran NAI 111 292 0 15 May 2019
Embedding Human Knowledge into Deep Neural Network via Attention Map Masahiro Mitsuhara Hiroshi Fukui Yusuke Sakashita Takanori Ogata Tsubasa Hirakawa Takayoshi Yamashita H. Fujiyoshi 59 72 0 09 May 2019
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 71 639 0 29 Mar 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 102 1,015 0 26 Feb 2019
Saliency Learning: Teaching the Model Where to Pay Attention Reza Ghaeini Xiaoli Z. Fern Hamed Shahbazi Prasad Tadepalli FAtt XAI 67 30 0 22 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric Xing Ameet Talwalkar AAML 64 80 0 18 Feb 2019
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded Ramprasaath R. Selvaraju Stefan Lee Yilin Shen Hongxia Jin Shalini Ghosh Larry Heck Dhruv Batra Devi Parikh FAtt VLM 64 254 0 11 Feb 2019
Neural Network Attributions: A Causal Perspective Aditya Chattopadhyay Piyushi Manupriya Anirban Sarkar V. Balasubramanian CML 50 146 0 06 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 93 203 0 06 Feb 2019
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 583 10,561 0 12 Dec 2018
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu Tim Rocktaschel Thomas Lukasiewicz Phil Blunsom LRM 414 638 0 04 Dec 2018
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh Joon Sik Kim Ian En-Hsu Yen Pradeep Ravikumar TDI 73 253 0 23 Nov 2018
Interpreting Black Box Predictions using Fisher Kernels Rajiv Khanna Been Kim Joydeep Ghosh Oluwasanmi Koyejo FAtt 81 104 0 23 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 136 1,967 0 08 Oct 2018
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding Kexin Yi Jiajun Wu Chuang Gan Antonio Torralba Pushmeet Kohli J. Tenenbaum NAI 84 609 0 04 Oct 2018
Model Reconstruction from Model Explanations S. Milli Ludwig Schmidt Anca Dragan Moritz Hardt FAtt 52 177 0 13 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 108 681 0 28 Jun 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 240 1,186 0 27 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 126 941 0 20 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 68 146 0 14 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 95 1,857 0 31 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 131 437 0 28 May 2018
Boolean Decision Rules via Column Generation S. Dash Oktay Gunluk Dennis L. Wei 67 174 0 24 May 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers Andy Shih Arthur Choi Adnan Darwiche FAtt 72 243 0 09 May 2018
Explainable Recommendation: A Survey and New Perspectives Yongfeng Zhang Xu Chen XAI LRM 79 876 0 30 Apr 2018