Interpreting Blackbox Models via Model Extraction

23 May 2017

Papers citing "Interpreting Blackbox Models via Model Extraction"

27 / 27 papers shown

Title
Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction Ruoyu Li Qing Li Yu Zhang Dan Zhao Xi Xiao Yong-jia Jiang 40 3 0 28 Mar 2024
BELLA: Black box model Explanations by Local Linear Approximations N. Radulovic Albert Bifet Fabian M. Suchanek FAtt 37 1 0 18 May 2023
Logic-Based Explainability in Machine Learning Sasha Rubin LRM XAI 50 39 0 24 Oct 2022
Dynamic Latent Separation for Deep Learning Yi-Lin Tuan Zih-Yun Chiu William Yang Wang 36 0 0 07 Oct 2022
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification E. Slany Yannik Ott Stephan Scheele Jan Paulus Ute Schmid 22 8 0 06 Apr 2022
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners K. Ramamurthy Amit Dhurandhar Dennis L. Wei Zaid Bin Tariq FAtt 38 3 0 02 Feb 2022
Information-theoretic Evolution of Model Agnostic Global Explanations Sukriti Verma Nikaash Puri Piyush B. Gupta Balaji Krishnamurthy FAtt 29 0 0 14 May 2021
Efficient Encrypted Inference on Ensembles of Decision Trees Kanthi Kiran Sarpatwar Karthik Nandakumar N. Ratha J. Rayfield Karthikeyan Shanmugam Sharath Pankanti Roman Vaculin FedML 22 5 0 05 Mar 2021
Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning Jiaheng Xie Xinyu Liu HAI 31 10 0 21 Dec 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 24 85 0 21 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 20 397 0 19 Oct 2020
Machine Guides, Human Supervises: Interactive Learning with Global Explanations Teodora Popordanoska Mohit Kumar Stefano Teso 21 21 0 21 Sep 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 30 625 0 01 Jul 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 41 371 0 30 Apr 2020
Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks Chaojie Ji Ruxin Wang Hongyan Wu 28 7 0 21 Apr 2020
Born-Again Tree Ensembles Thibaut Vidal Toni Pacheco Maximilian Schiffer 62 53 0 24 Mar 2020
Learning Global Transparent Models Consistent with Local Contrastive Explanations Tejaswini Pedapati Avinash Balakrishnan Karthikeyan Shanmugam Amit Dhurandhar FAtt 22 0 0 19 Feb 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 32 207 0 15 Jan 2020
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 34 205 0 27 Oct 2019
The Price of Interpretability Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin 23 33 0 08 Jul 2019
Model Agnostic Contrastive Explanations for Structured Data Amit Dhurandhar Tejaswini Pedapati Avinash Balakrishnan Pin-Yu Chen Karthikeyan Shanmugam Ruchi Puri FAtt 20 82 0 31 May 2019
Copying Machine Learning Classifiers Irene Unceta Jordi Nin O. Pujol 14 18 0 05 Mar 2019
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 20 30 0 05 Oct 2018
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach Yeounoh Chung Tim Kraska N. Polyzotis Ki Hyun Tae Steven Euijong Whang 17 129 0 16 Jul 2018
Optimal Piecewise Local-Linear Approximations Kartik Ahuja W. Zame M. Schaar FAtt 17 1 0 27 Jun 2018
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users Stefano Teso Kristian Kersting FAtt HAI 9 12 0 22 May 2018
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization Jaedeok Kim Ji-Hoon Seo FAtt 18 8 0 18 Sep 2017