Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

20 March 2021

Zhi Chen

Papers citing "Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges"

50 / 79 papers shown

Title
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users Julian Rosenberger Philipp Schröppel Sven Kruschel Mathias Kraus Patrick Zschech Maximilian Förster FAtt 24 0 0 11 May 2025
Prediction Models That Learn to Avoid Missing Values Lena Stempfle Anton Matsson Newton Mwai Fredrik D. Johansson 40 0 0 06 May 2025
Re-Imagining Multimodal Instruction Tuning: A Representation View Yiyang Liu James Liang Ruixiang Tang Yugyung Lee Majid Rabbani ... Raghuveer M. Rao Lifu Huang Dongfang Liu Qifan Wang Cheng Han 129 0 0 02 Mar 2025
Model Lakes Koyena Pal David Bau Renée J. Miller 63 0 0 24 Feb 2025
The Curious Case of Arbitrariness in Machine Learning Prakhar Ganesh Afaf Taik G. Farnadi 59 2 0 28 Jan 2025
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review Giovanni Ciatto Federico Sabbatini Andrea Agiollo Matteo Magnini Andrea Omicini 46 14 0 28 Jan 2025
Parallel Key-Value Cache Fusion for Position Invariant RAG Philhoon Oh Jinwoo Shin James Thorne 3DV 143 0 0 13 Jan 2025
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations Xin-Chao Xu Yi Qin Lu Mi Hao Wang X. Li 74 9 0 03 Jan 2025
A Tale of Two Imperatives: Privacy and Explainability Supriya Manna Niladri Sett 91 0 0 30 Dec 2024
Synthesizing Interpretable Control Policies through Large Language Model Guided Search Carlo Bosio Mark W. Mueller 26 0 0 07 Oct 2024
Amazing Things Come From Having Many Good Models Cynthia Rudin Chudi Zhong Lesia Semenova Margo Seltzer Ronald E. Parr Jiachang Liu Srikar Katta Jon Donnelly Harry Chen Zachery Boner 26 23 0 05 Jul 2024
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models Jayneel Parekh Quentin Bouniot Pavlo Mozharovskyi A. Newson Florence dÁlché-Buc SSL 61 1 0 01 Jul 2024
Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems Rohan R. Paleja Michael Munje K. Chang Reed Jensen Matthew C. Gombolay 32 2 0 07 Jun 2024
Efficient Exploration of the Rashomon Set of Rule Set Models Martino Ciaperoni Han Xiao A. Gionis 23 3 0 05 Jun 2024
Differential contributions of machine learning and statistical analysis to language and cognitive sciences Kun Sun Rong Wang 33 1 0 22 Apr 2024
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning F. Mumuni A. Mumuni AAML 37 5 0 11 Mar 2024
Social Interpretable Reinforcement Learning Leonardo Lucio Custode Giovanni Iacca OffRL 35 2 0 27 Jan 2024
Quantum Algorithms for the Pathwise Lasso J. F. Doriguello Debbie Lim Chi Seng Pun P. Rebentrost Tushar Vaidya 37 1 0 21 Dec 2023
A knowledge-driven AutoML architecture C. Cofaru Johan Loeckx 21 0 0 28 Nov 2023
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 24 0 0 20 Nov 2023
Interpretable Reinforcement Learning for Robotics and Continuous Control Rohan R. Paleja Letian Chen Yaru Niu Andrew Silva Zhaoxin Li ... K. Chang H. E. Tseng Yan Wang S. Nageshrao Matthew C. Gombolay 26 7 0 16 Nov 2023
Advancing Post Hoc Case Based Explanation with Feature Highlighting Eoin M. Kenny Eoin Delaney Markt. Keane 31 5 0 06 Nov 2023
Learning Optimal Classification Trees Robust to Distribution Shifts Nathan Justin S. Aghaei Andrés Gómez P. Vayanos OOD 33 0 0 26 Oct 2023
RecRec: Algorithmic Recourse for Recommender Systems Sahil Verma Ashudeep Singh Varich Boonsanong John P. Dickerson Chirag Shah 25 1 0 28 Aug 2023
Toward Transparent Sequence Models with Model-Based Tree Markov Model Chan Hsu Wei Huang Jun-Ting Wu Chih-Yuan Li Yihuang Kang 26 0 0 28 Jul 2023
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models Julien N. Siems Konstantin Ditschuneit Winfried Ripken Alma Lindborg Maximilian Schambach Johannes Otterbach Martin Genzel 19 6 0 19 May 2023
An Interpretable Loan Credit Evaluation Method Based on Rule Representation Learner Zi-yu Chen Xiaomeng Wang Yuanjiang Huang Tao Jia 31 1 0 03 Apr 2023
Towards Learning and Explaining Indirect Causal Effects in Neural Networks Abbaavaram Gowtham Reddy Saketh Bachu Harsh Nilesh Pathak Ben Godfrey V. Balasubramanian V. Varshaneya Satya Narayanan Kar CML 26 0 0 24 Mar 2023
ICICLE: Interpretable Class Incremental Continual Learning Dawid Rymarczyk Joost van de Weijer Bartosz Zieliñski Bartlomiej Twardowski CLL 24 28 0 14 Mar 2023
On the contribution of pre-trained models to accuracy and utility in modeling distributed energy resources H. Kazmi Pierre Pinson 11 0 0 22 Feb 2023
Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal M. Hashemi Ali Darejeh Francisco Cruz 37 3 0 07 Feb 2023
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks Ryan Thompson Amir Dezfouli Robert Kohn 29 4 0 02 Feb 2023
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts Mikolaj Sacha Dawid Rymarczyk Lukasz Struski Jacek Tabor Bartosz Zieliñski VLM 30 29 0 28 Jan 2023
Explainable Deep Reinforcement Learning: State of the Art and Challenges G. Vouros XAI 48 76 0 24 Jan 2023
Shapley variable importance cloud for machine learning models Yilin Ning Mingxuan Liu Nan Liu FAtt TDI 30 1 0 16 Dec 2022
Interpretability with full complexity by constraining feature information Kieran A. Murphy Danielle Bassett FAtt 27 5 0 30 Nov 2022
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction Montgomery Flora Corey K. Potvin A. McGovern Shawn Handler FAtt 26 4 0 18 Nov 2022
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 32 4 0 09 Nov 2022
Margin Optimal Classification Trees Federico DÓnofrio G. Grani Marta Monaci L. Palagi 21 10 0 19 Oct 2022
Superpolynomial Lower Bounds for Decision Tree Learning and Testing Caleb M. Koch Carmen Strassle Li-Yang Tan 24 8 0 12 Oct 2022
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations He Cheng Depeng Xu Shuhan Yuan Xintao Wu AI4TS 35 3 0 09 Oct 2022
Sparse PCA With Multiple Components Ryan Cory-Wright J. Pauphilet 25 2 0 29 Sep 2022
Computing Abductive Explanations for Boosted Trees Gilles Audemard Jean-Marie Lagniez Pierre Marquis N. Szczepanski 24 12 0 16 Sep 2022
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees Zichuan Liu Zichuan Liu Zhi Wang Yuanyang Zhu Chunlin Chen 55 5 0 15 Sep 2022
Seeking Interpretability and Explainability in Binary Activated Neural Networks Benjamin Leblanc Pascal Germain FAtt 29 1 0 07 Sep 2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy Giang Nguyen Mohammad Reza Taesiri Anh Totti Nguyen 30 42 0 26 Jul 2022
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning Damien Teney Maxime Peyrard Ehsan Abbasnejad 30 29 0 06 Jul 2022
Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases David Munechika Zijie J. Wang Jack Reidy Josh Rubin Krishna Gade K. Kenthapadi Duen Horng Chau MLAU 21 18 0 25 Jun 2022
Rectifying Mono-Label Boolean Classifiers S. Coste-Marquis Pierre Marquis 30 0 0 17 Jun 2022
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes Yishay Mansour Michal Moshkovitz Cynthia Rudin FAtt 29 3 0 09 Jun 2022