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Interpretability via Model Extraction

29 June 2017

Papers citing "Interpretability via Model Extraction"

50 / 68 papers shown

Title
Explainable AI the Latest Advancements and New Trends Bowen Long Enjie Liu Renxi Qiu Yanqing Duan XAI 679 2 0 11 May 2025
Model-Agnostic Policy Explanations with Large Language Models Zhang Xi-Jia Yue (Sophie) Guo Shufei Chen Simon Stepputtis Matthew C. Gombolay Katia Sycara Joseph Campbell LM&Ro LRM 243 3 0 08 Apr 2025
Models That Are Interpretable But Not TransparentInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025 Chudi Zhong Panyu Chen Cynthia Rudin AAML 217 0 0 26 Feb 2025
Recent Advances in Malware Detection: Graph Learning and Explainability Hossein Shokouhinejad Roozbeh Razavi-Far Hesamodin Mohammadian Mahdi Rabbani Samuel Ansong Griffin Higgins Ali Ghorbani AAML 464 10 0 14 Feb 2025
Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance AnalysisEuropean Conference on Artificial Intelligence (ECAI), 2024 Francesco Sovrano Michaël Lognoul Giulia Vilone 172 1 0 27 Aug 2024
A Theory of Interpretable ApproximationsAnnual Conference Computational Learning Theory (COLT), 2024 Marco Bressan Nicolò Cesa-Bianchi Emmanuel Esposito Yishay Mansour Shay Moran Maximilian Thiessen FAtt 174 6 0 15 Jun 2024
Towards a theory of model distillation Enric Boix-Adserà FedML VLM 187 13 0 14 Mar 2024
Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation Xijia Zhang Yue (Sophie) Guo Simon Stepputtis Katia Sycara Joseph Campbell LLMAG LM&Ro 165 2 0 29 Nov 2023
Explaining Agent Behavior with Large Language Models Xijia Zhang Yue (Sophie) Guo Simon Stepputtis Katia Sycara Joseph Campbell LM&Ro LLMAG 177 7 0 19 Sep 2023
Dual Student Networks for Data-Free Model StealingInternational Conference on Learning Representations (ICLR), 2023 James Beetham Navid Kardan Lin Wang M. Shah 219 24 0 18 Sep 2023
i-Align: an interpretable knowledge graph alignment modelData mining and knowledge discovery (DMKD), 2023 Bayu Distiawan Trisedya Flora D. Salim Jeffrey Chan Damiano Spina Falk Scholer Mark Sanderson 164 11 0 26 Aug 2023
Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic LearningIEEE Transactions on Visualization and Computer Graphics (TVCG), 2023 Ouyang Yang Yuchen Wu He Wang Chenyang Zhang Furui Cheng Chang Jiang Lixia Jin Yuanwu Cao Qu Li 135 15 0 23 Jul 2023
Properly Learning Decision Trees with Queries Is NP-HardIEEE Annual Symposium on Foundations of Computer Science (FOCS), 2023 Caleb M. Koch Carmen Strassle Li-Yang Tan 152 9 0 09 Jul 2023
Interpretable Differencing of Machine Learning ModelsConference on Uncertainty in Artificial Intelligence (UAI), 2023 Swagatam Haldar Diptikalyan Saha Dennis L. Wei Rahul Nair Elizabeth M. Daly 172 1 0 10 Jun 2023
Implementing Responsible AI: Tensions and Trade-Offs Between Ethics AspectsIEEE International Joint Conference on Neural Network (IJCNN), 2023 Conrad Sanderson David M. Douglas Qinghua Lu 158 18 0 17 Apr 2023
ViT-Calibrator: Decision Stream Calibration for Vision TransformerAAAI Conference on Artificial Intelligence (AAAI), 2023 Lin Chen Zhijie Jia Tian Qiu Lechao Cheng Jie Lei Zunlei Feng Min-Gyoo Song 263 3 0 10 Apr 2023
Concept Learning for Interpretable Multi-Agent Reinforcement LearningConference on Robot Learning (CoRL), 2023 Renos Zabounidis Joseph Campbell Simon Stepputtis Dana Hughes Katia Sycara 158 21 0 23 Feb 2023
(When) Are Contrastive Explanations of Reinforcement Learning Helpful? Sanjana Narayanan Isaac Lage Finale Doshi-Velez OffRL 64 1 0 14 Nov 2022
Trade-off Between Efficiency and Consistency for Removal-based ExplanationsNeural Information Processing Systems (NeurIPS), 2022 Yifan Zhang Haowei He Zhiyuan Tan Yang Yuan FAtt 321 6 0 31 Oct 2022
Logic-Based Explainability in Machine Learning Sasha Rubin LRM XAI 408 57 0 24 Oct 2022
Superpolynomial Lower Bounds for Decision Tree Learning and TestingACM-SIAM Symposium on Discrete Algorithms (SODA), 2022 Caleb M. Koch Carmen Strassle Li-Yang Tan 207 9 0 12 Oct 2022
Explaining Machine Learning Models in Natural Conversations: Towards a Conversational XAI Agent Van Bach Nguyen Jorg Schlotterer C. Seifert AILaw 144 17 0 06 Sep 2022
A Query-Optimal Algorithm for Finding CounterfactualsInternational Conference on Machine Learning (ICML), 2022 Guy Blanc Caleb M. Koch Jane Lange Li-Yang Tan 180 5 0 14 Jul 2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations Chirag Agarwal Dan Ley Satyapriya Krishna Eshika Saxena Martin Pawelczyk Nari Johnson Isha Puri Marinka Zitnik Himabindu Lakkaraju XAI 398 168 0 22 Jun 2022
A Human-Centric Take on Model MonitoringAAAI Conference on Human Computation & Crowdsourcing (HCOMP), 2022 Murtuza N. Shergadwala Himabindu Lakkaraju K. Kenthapadi 178 16 0 06 Jun 2022
Neural Basis Models for InterpretabilityNeural Information Processing Systems (NeurIPS), 2022 Filip Radenovic Abhimanyu Dubey D. Mahajan FAtt 286 61 0 27 May 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc ExplanationsAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022 Jessica Dai Sohini Upadhyay Ulrich Aïvodji Stephen H. Bach Himabindu Lakkaraju 209 61 0 15 May 2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective Himabindu Lakkaraju Dylan Slack Yuxin Chen Chenhao Tan Sameer Singh LRM 201 72 0 03 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 644 237 0 03 Feb 2022
Forward Composition Propagation for Explainable Neural ReasoningIEEE Computational Intelligence Magazine (IEEE CIM), 2021 Isel Grau Gonzalo Nápoles M. Bello Yamisleydi Salgueiro A. Jastrzębska 105 2 0 23 Dec 2021
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers Zunlei Feng Jiacong Hu Sai Wu Xiaotian Yu Mingli Song Xiuming Zhang 228 15 0 09 Dec 2021
Provably efficient, succinct, and precise explanationsNeural Information Processing Systems (NeurIPS), 2021 Guy Blanc Jane Lange Li-Yang Tan FAtt 252 39 0 01 Nov 2021
The Role of Explainability in Assuring Safety of Machine Learning in HealthcareIEEE Transactions on Emerging Topics in Computing (TETC), 2021 Yan Jia John McDermid T. Lawton Ibrahim Habli 217 62 0 01 Sep 2021
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment AnalysisIEEE Transactions on Visualization and Computer Graphics (TVCG), 2021 Xingbo Wang Jianben He Zhihua Jin Muqiao Yang Yong Wang Huamin Qu 160 89 0 17 Jul 2021
BODAME: Bilevel Optimization for Defense Against Model Extraction Y. Mori Atsushi Nitanda Akiko Takeda MIACV 228 4 0 11 Mar 2021
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and ApplicationsInformation Fusion (Inf. Fusion), 2021 Yu-Liang Chou Catarina Moreira P. Bruza Chun Ouyang Joaquim A. Jorge CML 315 213 0 07 Mar 2021
Towards the Unification and Robustness of Perturbation and Gradient Based ExplanationsInternational Conference on Machine Learning (ICML), 2021 Sushant Agarwal S. Jabbari Chirag Agarwal Sohini Upadhyay Zhiwei Steven Wu Himabindu Lakkaraju FAtt AAML 271 70 0 21 Feb 2021
Why model why? Assessing the strengths and limitations of LIME Jurgen Dieber S. Kirrane FAtt 210 127 0 30 Nov 2020
Robust and Stable Black Box ExplanationsInternational Conference on Machine Learning (ICML), 2020 Himabindu Lakkaraju Nino Arsov Osbert Bastani AAML FAtt 160 90 0 12 Nov 2020
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making S. McGrath Parth Mehta Alexandra Zytek Isaac Lage Himabindu Lakkaraju UD 254 26 0 12 Nov 2020
Designing Interpretable Approximations to Deep Reinforcement Learning Nathan Dahlin K. C. Kalagarla Nikhil Naik Rahul Jain Pierluigi Nuzzo 169 10 0 28 Oct 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 177 97 0 21 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 279 182 0 08 Oct 2020
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action SystemsIEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2020 Yashesh D. Dhebar Kalyanmoy Deb S. Nageshrao Ling Zhu Dimitar Filev 205 17 0 20 Sep 2020
Principles and Practice of Explainable Machine LearningFrontiers in Big Data (Front. Big Data), 2020 Vaishak Belle I. Papantonis FaML 206 518 0 18 Sep 2020
Contextual Semantic InterpretabilityAsian Conference on Computer Vision (ACCV), 2020 Diego Marcos Ruth C. Fong Sylvain Lobry Rémi Flamary Nicolas Courty D. Tuia SSL 220 28 0 18 Sep 2020
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses Kaivalya Rawal Himabindu Lakkaraju 283 12 0 15 Sep 2020
Reliable Post hoc Explanations: Modeling Uncertainty in ExplainabilityNeural Information Processing Systems (NeurIPS), 2020 Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 511 197 0 11 Aug 2020
Generative causal explanations of black-box classifiersNeural Information Processing Systems (NeurIPS), 2020 Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 225 76 0 24 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 523 299 0 29 May 2020