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Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting.
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Very simple classification rules perform well on most commonly used datasets
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A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study
Jean-Roger Le Gall, Stanley Lemeshow, and Fabienne Saulnier · 1993
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Fast effective rule induction
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Extracting tree-structured representations of trained networks
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Induction of ripple-down rules applied to modeling large databases
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Learning Vector Quantization
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Optimal decision trees
K. P. Bennett and J. A. Blue · 1996
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Extracting comprehensible models from trained neural networks
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From data mining to knowledge discovery in databases
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth · 1996
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SLIQ: A fast scalable classifier for data mining
Manish Mehta, Rakesh Agrawal, and Jorma Rissanen · 1996
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Induction of shallow decision trees
David Dobkin, Truxton Fulton, Dimitrios Gunopulos, Simon Kasif, and Steven Salzberg · 1997
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Split selection methods for classification trees
Wei-Yin Loh and Yu-Shan Shih · 1997
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Relational reinforcement learning
Sašo Džeroski, Luc De Raedt, and Hendrik Blockeel · 1998
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Generating accurate rule sets without global optimization
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Artificial neural networks for solving ordinary and partial differential equations
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Integrating classification and association rule mining
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Boosting the margin: A new explanation for the effectiveness of voting methods
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Bump hunting in high-dimensional data
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The TIMI risk score for unstable angina/non–ST elevation MI: a method for prognostication and therapeutic decision making
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CRISP-DM 1.0 - step-by-step data mining guide
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Neural-network methods for boundary value problems with irregular boundaries
I. E. Lagaris, A. C. Likas, and D. G. Papageorgiou · 2000
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GA tree: genetically evolved decision trees
Athanassios Papagelis and Dimitrios Kalles · 2000
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Nonlinear dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin de Silva, and John C. Langford · 2000
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2001
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Leo Breiman et al · 2001
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Relational reinforcement learning
Sašo Džeroski, Luc De Raedt, and Kurt Driessens · 2001
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Validation of clinical classification schemes for predicting stroke: results from the national registry of atrial fibrillation
Brian F Gage, Amy D Waterman, William Shannon, Michael Boechler, Michael W Rich, and Martha J Radford · 2001
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Cmar: Accurate and efficient classification based on multiple class-association rules
Wenmin Li, Jiawei Han, and Jian Pei · 2001
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Understanding and mitigating gradient pathologies in physics-informed neural networks
Sifan Wang, Yujun Teng, and Paris Perdikaris · 2001
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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Stochastic gradient boosting
Jerome H Friedman · 2002
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Genealogy of the “Grandmother Cell”
Charles C. Gross · 2002
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Empirical margin distributions and bounding the generalization error of combined classifiers
Vladimir Koltchinskii and Dmitry Panchenko · 2002
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The set covering machine
Mario Marchand and John Shawe-Taylor · 2002
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The covering number in learning theory
Ding-Xuan Zhou · 2002
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Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
David L. Donoho and Carrie Grimes · 2003
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The decision list machine
Marina Sokolova, Mario Marchand, Nathalie Japkowicz, and John S Shawe-taylor · 2003
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Cpar: Classification based on predictive association rules
Xiaoxin Yin and Jiawei Han · 2003
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Learning with decision lists of data-dependent features
Mario Marchand and Marina Sokolova · 2005
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SAPS 3 — from evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission
Rui P Moreno, Philipp GH Metnitz, Eduardo Almeida, Barbara Jordan, Peter Bauer, Ricardo Abizanda Campos, Gaetano Iapichino, David Edbrooke, Maurizia Capuzzo, Jean-Roger Le Gall, et al · 2005
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Component selection and smoothing in multivariate nonparametric regression
Yi Lin, Hao Helen Zhang, et al · 2006
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Gaussian process approximations of stochastic differential equations
Cedric Archambeau, Dan Cornford, Manfred Opper, and John Shawe-Taylor · 2007
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Mining optimal decision trees from itemset lattices
Siegfried Nijssen and Elisa Fromont · 2007
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Learning a nonlinear embedding by preserving class neighbourhood structure
Ruslan Salakhutdinov and Geoff Hinton · 2007
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A comparison of methods for the fitting of generalized additive models
Harald Binder and Gerhard Tutz · 2008
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A fast way to produce optimal fixed-depth decision trees
A. Farhangfar, R. Greiner, and M. Zinkevich · 2008
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Classification tree analysis using target
J Brian Gray and Guangzhe Fan · 2008
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Adaptive treatment of epilepsy via batch-mode reinforcement learning
Arthur Guez, Robert D Vincent, Massimo Avoli, and Joelle Pineau · 2008
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Non-parametric policy gradients: A unified treatment of propositional and relational domains
Kristian Kersting and Kurt Driessens · 2008
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Chest pain in the emergency room: value of the heart score
AJ Six, BE Backus, and JC Kelder · 2008
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Learning deep architectures for AI
Yoshua Bengio · 2009
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On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Sham M Kakade, Karthik Sridharan, and Ambuj Tewari · 2009
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High-dimensional additive modeling
Lukas Meier, Sara Van de Geer, Peter Bühlmann, et al · 2009
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Sparse additive models
Pradeep Ravikumar, John Lafferty, Han Liu, and Larry Wasserman · 2009
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Margin-based ranking and an equivalence between AdaBoost and RankBoost
Cynthia Rudin and Robert E. Schapire · 2009
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
Kilian Q Weinberger and Lawrence K Saul · 2009
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Optimal constraint-based decision tree induction from itemset lattices
S. Nijssen and E. Fromont · 2010
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Smoothness, low noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
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Structure of association rule classifiers: a review
Koen Vanhoof and Benoît Depaire · 2010
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Optimal decision lists using sat
Jinqiang Yu, Alexey Ignatiev, Pierre Le Bodic, and Peter J Stuckey · 2010
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Prototype Selection for Interpretable Classification
Jacob Bien and Robert Tibshirani · 2011
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On equivalence relationships between classification and ranking algorithms
Şeyda Ertekin and Cynthia Rudin · 2011
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Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
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This Looks Like That, Because… Explaining Prototypes for Interpretable Image Recognition
Meike Nauta, Annemarie Jutte, Jesper Provoost, and Christin Seifert · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
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PDE-constrained gaussian process model on material removal rate of wire saw slicing process
Hongxu Zhao, Ran Jin, Su Wu, and Jianjun Shi · 2011
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Challenges and opportunities with big data: A white paper prepared for the computing community consortium committee of the computing research association
D. Agrawal, P. Bernstein, E. Bertino, S. Davidson, U. Dayal, M. Franklin, and J. Widom · 2012
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Guillaume Desjardins, Aaron Courville, and Yoshua Bengio · 2012
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The thing that we tried didn’t work very well: Deictic representation in reinforcement learning
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Interpretable convolutional neural networks
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Deep learning predicts hip fracture using confounding patient and healthcare variables
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Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
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The age of secrecy and unfairness in recidivism prediction
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Ethical implementation of artificial intelligence to select embryos in In Vitro Fertilization
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Mathematical optimization in classification and regression trees
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Characterizing fairness over the set of good models under selective labels
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How to represent part-whole hierarchies in a neural network
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Deep reinforcement learning for autonomous driving: A survey
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Forward stability and model path selection
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Algorithm’s ‘unexpected’ weakness raises larger concerns about AI’s potential in broader populations
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Interpretable and trustworthy deepfake detection via dynamic prototypes
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