Supersparse Linear Integer Models for Optimized Medical Scoring Systems

15 February 2015

Papers citing "Supersparse Linear Integer Models for Optimized Medical Scoring Systems"

50 / 51 papers shown

Title
PointExplainer: Towards Transparent Parkinson's Disease Diagnosis Xuechao Wang S. Nõmm Junqing Huang Kadri Medijainen A. Toomela Michael Ruzhansky AAML FAtt 26 0 0 04 May 2025
Model Lakes Koyena Pal David Bau Renée J. Miller 67 0 0 24 Feb 2025
Probabilistic Scoring Lists for Interpretable Machine Learning Jonas Hanselle Stefan Heid Zhigang Zeng Eyke Hüllermeier 33 0 0 31 Jul 2024
Learning Interpretable Differentiable Logic Networks Chang Yue N. Jha NAI AI4CE 29 0 0 04 Jul 2024
Distilled Datamodel with Reverse Gradient Matching Jingwen Ye Ruonan Yu Songhua Liu Xinchao Wang DD 41 3 0 22 Apr 2024
Explainable Depression Symptom Detection in Social Media Eliseo Bao Souto Anxo Perez Javier Parapar 30 5 0 20 Oct 2023
Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction Yang Wu Xurui Li Xuhong Zhang Yangyang Kang Changlong Sun Xiaozhong Liu 32 3 0 06 Sep 2023
Learning Optimal Fair Scoring Systems for Multi-Class Classification Julien Rouzot Julien Ferry Marie-José Huguet FaML 19 8 0 11 Apr 2023
FedScore: A privacy-preserving framework for federated scoring system development Siqi Li Yilin Ning M. Ong Bibhas Chakraborty Chuan Hong ... Han Yuan Mingxuan Liu Daniel M. Buckland Yongju Chen Nan Liu FedML 29 7 0 01 Mar 2023
Who Should Predict? Exact Algorithms For Learning to Defer to Humans Hussein Mozannar Hunter Lang Dennis L. Wei P. Sattigeri Subhro Das David Sontag 30 41 0 15 Jan 2023
FasterRisk: Fast and Accurate Interpretable Risk Scores Jiachang Liu Chudi Zhong Boxuan Li Margo Seltzer Cynthia Rudin 44 16 0 12 Oct 2022
Leveraging Explanations in Interactive Machine Learning: An Overview Stefano Teso Öznur Alkan Wolfgang Stammer Elizabeth M. Daly XAI FAtt LRM 26 62 0 29 Jul 2022
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction Vinith Suriyakumar Marzyeh Ghassemi Berk Ustun 35 6 0 04 Jun 2022
Feature subset selection for kernel SVM classification via mixed-integer optimization Ryuta Tamura Yuichi Takano Ryuhei Miyashiro 26 2 0 28 May 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 38 78 0 06 May 2022
Sparse Bayesian Optimization Sulin Liu Qing Feng David Eriksson Benjamin Letham E. Bakshy 30 7 0 03 Mar 2022
Interpretable Low-Resource Legal Decision Making R. Bhambhoria Hui Liu Samuel Dahan Xiao-Dan Zhu ELM AILaw 29 9 0 01 Jan 2022
Learning Optimal Predictive Checklists Haoran Zhang Q. Morris Berk Ustun Marzyeh Ghassemi 26 11 0 02 Dec 2021
Optimal randomized classification trees R. Blanquero E. Carrizosa Antonios Tsourdos Dolores Romero Morales 11 47 0 19 Oct 2021
Shapley variable importance clouds for interpretable machine learning Yilin Ning M. Ong Bibhas Chakraborty B. Goldstein Daniel Ting Roger Vaughan Nan Liu FAtt 27 69 0 06 Oct 2021
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies F. Xie Han Yuan Yilin Ning M. Ong Mengling Feng W. Hsu B. Chakraborty Nan Liu 27 83 0 21 Jul 2021
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang 19 41 0 04 Jun 2021
Model Learning with Personalized Interpretability Estimation (ML-PIE) M. Virgolin A. D. Lorenzo Francesca Randone Eric Medvet M. Wahde 24 29 0 13 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 653 0 20 Mar 2021
Interpretability and Explainability: A Machine Learning Zoo Mini-tour Ricards Marcinkevics Julia E. Vogt XAI 28 119 0 03 Dec 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
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 30 51 0 03 Sep 2020
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction Caroline Linjun Wang Bin Han Bhrij Patel Cynthia Rudin FaML HAI 59 84 0 08 May 2020
Sparsity in Optimal Randomized Classification Trees R. Blanquero E. Carrizosa Cristina Molero-Río Dolores Romero Morales 25 45 0 21 Feb 2020
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 32 54 0 24 Aug 2019
On the Existence of Simpler Machine Learning Models Lesia Semenova Cynthia Rudin Ronald E. Parr 26 85 0 05 Aug 2019
A study on the Interpretability of Neural Retrieval Models using DeepSHAP Zeon Trevor Fernando Jaspreet Singh Avishek Anand FAtt AAML 16 68 0 15 Jul 2019
The Price of Interpretability Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin 23 33 0 08 Jul 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 38 19 0 10 May 2019
Interpretability with Accurate Small Models Abhishek Ghose Balaraman Ravindran 20 1 0 04 May 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 17 21 0 01 Apr 2019
How to improve the interpretability of kernel learning Jinwei Zhao Qizhou Wang Yufei Wang Yu Liu Zhenghao Shi Xinhong Hei FAtt 17 0 0 21 Nov 2018
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 20 30 0 05 Oct 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 28 539 0 18 Sep 2018
Confounding-Robust Policy Improvement Nathan Kallus Angela Zhou CML OffRL 40 152 0 22 May 2018
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making Michael Veale Max Van Kleek Reuben Binns 25 409 0 03 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 36 241 0 02 Feb 2018
Interpretability via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 16 129 0 29 Jun 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 27 170 0 23 May 2017
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 60 195 0 06 Apr 2017
Simple rules for complex decisions Jongbin Jung Connor Concannon Ravi Shroff Sharad Goel D. Goldstein CML 21 104 0 15 Feb 2017
Programs as Black-Box Explanations Sameer Singh Marco Tulio Ribeiro Carlos Guestrin FAtt 16 54 0 22 Nov 2016
Learning Optimized Risk Scores Berk Ustun Cynthia Rudin 17 82 0 01 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 17 16,646 0 16 Feb 2016
Learning Optimized Or's of And's Tong Wang Cynthia Rudin 19 25 0 06 Nov 2015