Supersparse Linear Integer Models for Optimized Medical Scoring Systems

15 February 2015

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

50 / 122 papers shown

Title
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data F. Xie Yilin Ning Han Yuan B. Goldstein M. Ong Nan Liu B. Chakraborty 15 16 0 13 Jun 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 30 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
CACTUS: Detecting and Resolving Conflicts in Objective Functions Subhajit Das Alex Endert 27 0 0 13 Mar 2021
Learning Interpretable Concept-Based Models with Human Feedback Isaac Lage Finale Doshi-Velez 22 24 0 04 Dec 2020
Interpretability and Explainability: A Machine Learning Zoo Mini-tour Ricards Marcinkevics Julia E. Vogt XAI 28 119 0 03 Dec 2020
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 25 752 0 16 Nov 2020
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science Adam J. Johs Denise E. Agosto Rosina O. Weber 20 6 0 13 Nov 2020
Model-Agnostic Explanations using Minimal Forcing Subsets Xing Han Joydeep Ghosh AAML 6 4 0 01 Nov 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 22 397 0 19 Oct 2020
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think! Jack Hessel Lillian Lee 29 72 0 13 Oct 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 33 51 0 03 Sep 2020
Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction David Leslie 27 67 0 15 Aug 2020
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing Max Biggs Wei-Ju Sun M. Ettl 14 35 0 03 Jul 2020
From Predictions to Decisions: Using Lookahead Regularization Nir Rosenfeld Sophie Hilgard S. Ravindranath David C. Parkes 22 20 0 20 Jun 2020
The Backbone Method for Ultra-High Dimensional Sparse Machine Learning Dimitris Bertsimas V. Digalakis 35 10 0 11 Jun 2020
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts R. Akrour Davide Tateo Jan Peters 28 21 0 10 Jun 2020
Explanations of Black-Box Model Predictions by Contextual Importance and Utility S. Anjomshoae Kary Främling A. Najjar 20 31 0 30 May 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 64 84 0 08 May 2020
Optimal Local Explainer Aggregation for Interpretable Prediction Qiaomei Li Rachel Cummings Yonatan Dov Mintz 11 0 0 20 Mar 2020
Sparsity in Optimal Randomized Classification Trees R. Blanquero E. Carrizosa Cristina Molero-Río Dolores Romero Morales 33 45 0 21 Feb 2020
Interactivity and Transparency in Medical Risk Assessment with Supersparse Linear Integer Models H. Profitlich Daniel Sonntag 9 3 0 26 Nov 2019
Predictive Multiplicity in Classification Charles Marx Flavio du Pin Calmon Berk Ustun 36 136 0 14 Sep 2019
Learning Fair Rule Lists Ulrich Aïvodji Julien Ferry Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 18 10 0 09 Sep 2019
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 21 68 0 15 Jul 2019
Optimal Explanations of Linear Models Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin FAtt 20 2 0 08 Jul 2019
The Price of Interpretability Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin 23 33 0 08 Jul 2019
Quickly Finding the Best Linear Model in High Dimensions Yahya Sattar Samet Oymak 24 8 0 03 Jul 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 30 345 0 11 Jun 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning Patrick Hall Navdeep Gill N. Schmidt SILM XAI FaML 11 28 0 08 Jun 2019
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 30 383 0 13 May 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
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability Christoph Molnar Giuseppe Casalicchio B. Bischl FAtt 11 59 0 08 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 25 21 0 01 Apr 2019
Assessing the Local Interpretability of Machine Learning Models Dylan Slack Sorelle A. Friedler C. Scheidegger Chitradeep Dutta Roy FAtt 10 71 0 09 Feb 2019
An Evaluation of the Human-Interpretability of Explanation Isaac Lage Emily Chen Jeffrey He Menaka Narayanan Been Kim Sam Gershman Finale Doshi-Velez FAtt XAI 18 151 0 31 Jan 2019
Rank-one Convexification for Sparse Regression Alper Atamtürk A. Gómez 11 50 0 29 Jan 2019
Learning Interpretable Models with Causal Guarantees Carolyn Kim Osbert Bastani FaML OOD CML 22 17 0 24 Jan 2019
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation Xuan Liu Xiaoguang Wang Stan Matwin HAI 14 97 0 28 Dec 2018
Interpretable Optimal Stopping D. Ciocan V. Mišić 24 42 0 18 Dec 2018
An Interpretable Model with Globally Consistent Explanations for Credit Risk Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang FAtt 15 93 0 30 Nov 2018
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead Cynthia Rudin ELM FaML 22 218 0 26 Nov 2018
How to improve the interpretability of kernel learning Jinwei Zhao Qizhou Wang Yufei Wang Yu Liu Zhenghao Shi Xinhong Hei FAtt 19 0 0 21 Nov 2018
How to Use Heuristics for Differential Privacy Seth Neel Aaron Roth Zhiwei Steven Wu 11 26 0 19 Nov 2018
Deep Weighted Averaging Classifiers Dallas Card Michael J.Q. Zhang Hao Tang 14 41 0 06 Nov 2018
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 28 30 0 05 Oct 2018