Learning Certifiably Optimal Rule Lists for Categorical Data

6 April 2017

Papers citing "Learning Certifiably Optimal Rule Lists for Categorical Data"

50 / 101 papers shown

Title
Justicia: A Stochastic SAT Approach to Formally Verify Fairness Bishwamittra Ghosh D. Basu Kuldeep S. Meel 19 36 0 14 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 30 51 0 03 Sep 2020
Counterfactual Explanations for Machine Learning on Multivariate Time Series Data E. Ates Burak Aksar V. Leung A. Coskun AI4TS 51 65 0 25 Aug 2020
Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction David Leslie 27 67 0 15 Aug 2020
Safety design concepts for statistical machine learning components toward accordance with functional safety standards Akihisa Morikawa Yamato Matsubara 12 3 0 04 Aug 2020
Diverse Rule Sets Guangyi Zhang Aristides Gionis 13 19 0 17 Jun 2020
Generalized and Scalable Optimal Sparse Decision Trees Jimmy J. Lin Chudi Zhong Diane Hu Cynthia Rudin Margo Seltzer 11 139 0 15 Jun 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
Layerwise Knowledge Extraction from Deep Convolutional Networks S. Odense Artur Garcez FAtt 16 9 0 19 Mar 2020
Vulnerabilities of Connectionist AI Applications: Evaluation and Defence Christian Berghoff Matthias Neu Arndt von Twickel AAML 24 23 0 18 Mar 2020
Interpretable Companions for Black-Box Models Dan-qing Pan Tong Wang Satoshi Hara FaML 17 8 0 10 Feb 2020
A Framework for Democratizing AI Shakkeel Ahmed Ravi Mula S. Dhavala 27 9 0 01 Jan 2020
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group) Zied Bouraoui Antoine Cornuéjols Thierry Denoeux Sebastien Destercke Didier Dubois ... Jérôme Mengin H. Prade Steven Schockaert M. Serrurier Christel Vrain 21 13 0 13 Dec 2019
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization Zhuo Wang Wei Zhang Ning Liu Jianyong Wang 11 29 0 10 Dec 2019
LIBRE: Learning Interpretable Boolean Rule Ensembles Graziano Mita Paolo Papotti Maurizio Filippone Pietro Michiardi 6 16 0 15 Nov 2019
Thirteen Simple Steps for Creating An R Package with an External C++ Library Dirk Eddelbuettel 6 0 0 14 Nov 2019
MonoNet: Towards Interpretable Models by Learning Monotonic Features An-phi Nguyen María Rodríguez Martínez FAtt 16 13 0 30 Sep 2019
Learning Fair Rule Lists Ulrich Aïvodji Julien Ferry Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 16 10 0 09 Sep 2019
The Next 700 Policy Miners: A Universal Method for Building Policy Miners C. C. Jiménez Luca Corinzia Thilo Weghorn David Basin 14 15 0 16 Aug 2019
Visualizing Image Content to Explain Novel Image Discovery Jake H. Lee K. Wagstaff 11 3 0 14 Aug 2019
Measurable Counterfactual Local Explanations for Any Classifier Adam White Artur Garcez FAtt 17 99 0 08 Aug 2019
FairSight: Visual Analytics for Fairness in Decision Making Yongsu Ahn Y. Lin 6 121 0 01 Aug 2019
An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability X. Hunt Ralph Abbey Ricky Tharrington J. Huiskens Nina Wesdorp MedIm 15 4 0 17 Jul 2019
Characterization of Overlap in Observational Studies Michael Oberst Fredrik D. Johansson Dennis L. Wei Tian Gao G. Brat David Sontag Kush R. Varshney CML 14 22 0 09 Jul 2019
On Validating, Repairing and Refining Heuristic ML Explanations Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt LRM 6 62 0 04 Jul 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 24 345 0 11 Jun 2019
Enumeration of Distinct Support Vectors for Interactive Decision Making Kentaro Kanamori Satoshi Hara Masakazu Ishihata Hiroki Arimura 8 0 0 05 Jun 2019
Generalized Linear Rule Models Dennis L. Wei S. Dash Tian Gao Oktay Gunluk 13 63 0 05 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis Cynthia Rudin David Carlson HAI 17 34 0 04 Jun 2019
Learning Representations by Humans, for Humans Sophie Hilgard Nir Rosenfeld M. Banaji Jack Cao David C. Parkes OCL HAI AI4CE 31 29 0 29 May 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 35 19 0 10 May 2019
Interpretability with Accurate Small Models Abhishek Ghose Balaraman Ravindran 15 1 0 04 May 2019
Interpretable multiclass classification by MDL-based rule lists Hugo Manuel Proença M. Leeuwen 11 48 0 01 May 2019
Optimal Sparse Decision Trees Xiyang Hu Cynthia Rudin Margo Seltzer 11 173 0 29 Apr 2019
Learning Optimal Decision Trees from Large Datasets Florent Avellaneda 6 3 0 12 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 17 21 0 01 Apr 2019
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification Blaž Škrlj Matej Martinc Jan Kralj Nada Lavrac Senja Pollak 11 44 0 01 Feb 2019
Learning Interpretable Models with Causal Guarantees Carolyn Kim Osbert Bastani FaML OOD CML 14 17 0 24 Jan 2019
Interpretable Optimal Stopping D. Ciocan V. Mišić 16 42 0 18 Dec 2018
Abduction-Based Explanations for Machine Learning Models Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt 12 219 0 26 Nov 2018
Actionable Recourse in Linear Classification Berk Ustun Alexander Spangher Yang Liu FaML 28 539 0 18 Sep 2018
Investigating Human + Machine Complementarity for Recidivism Predictions S. Tan Julius Adebayo K. Quinn Ece Kamar FaML 16 54 0 28 Aug 2018
Boolean Decision Rules via Column Generation S. Dash Oktay Gunluk Dennis L. Wei 22 174 0 24 May 2018
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results Beau Coker Cynthia Rudin Gary King 19 29 0 23 Apr 2018
A Minimax Surrogate Loss Approach to Conditional Difference Estimation Siong Thye Goh Cynthia Rudin 17 6 0 10 Mar 2018
Interpreting Complex Regression Models Noa Avigdor-Elgrabli Alex Libov M. Viderman R. Wolff 25 1 0 26 Feb 2018
Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute Tong Wang 31 8 0 12 Feb 2018
Considerations When Learning Additive Explanations for Black-Box Models S. Tan Giles Hooker Paul Koch Albert Gordo R. Caruana FAtt 27 20 0 26 Jan 2018
An Optimization Approach to Learning Falling Rule Lists Chaofan Chen Cynthia Rudin 26 39 0 06 Oct 2017
Learning Optimized Risk Scores Berk Ustun Cynthia Rudin 17 82 0 01 Oct 2016