Fetching the paper…
Reading the bibliography…
This work addresses the situation where a black-box model with good predictive performance is chosen over its interpretable competitors, and we show interpretability is still achievable in this case.
A desicion-theoretic generalization of on-line learning and an application to boosting
Freund, Y. and Schapire, R. E · 1995
Earlier work this paper cites.
The effect of exposure to violence on young children
Osofsky, J. D · 1995
Earlier work this paper cites.
Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Kohavi, R · 1996
Earlier work this paper cites.
A hybrid approach of neural network and memory-based learning to data mining
Shin, C.-K., Yun, U. T., Kim, H. K., and Park, S. C · 2000
Earlier work this paper cites.
Classification and regression by randomforest
Liaw, A., Wiener, M., et al · 2002
Earlier work this paper cites.
A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts
Hua, Z. and Zhang, B · 2006
Earlier work this paper cites.
The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
Yeh, I.-C. and Lien, C.-h · 2009
Earlier work this paper cites.
Finding a short and accurate decision rule in disjunctive normal form by exhaustive search
Rijnbeek, P. R. and Kors, J. A · 2010
Earlier work this paper cites.
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Gacto, M. J., Alcalá, R., and Herrera, F · 2011
Earlier work this paper cites.
A hierarchical model for association rule mining of sequential events: An approach to automated medical symptom prediction
McCormick, T., Rudin, C., and Madigan, D · 2011
Earlier work this paper cites.
Uci machine learning repository, 2013
Lichman, M · 2013
Earlier work this paper cites.
C50: C5. 0 decision trees and rule-based models
Kuhn, M., Weston, S., Coulter, N., and Quinlan, R · 2014
Earlier work this paper cites.
C4. 5: programs for machine learning
Quinlan, J. R · 2014
Cited alongside, same era.
A two-step method to construct credit scoring models with data mining techniques
Koh, H. C., Tan, W. C., and Goh, C. P · 2015
Cited alongside, same era.
From group to individual labels using deep features
Kotzias, D., Denil, M., De Freitas, N., and Smyth, P · 2015
Cited alongside, same era.
Trading interpretability for accuracy: Oblique treed sparse additive models
Wang, J., Fujimaki, R., and Motohashi, Y · 2015
Cited alongside, same era.
Auditing black-box models for indirect influence
Adler, P., Falk, C., Friedler, S. A., Rybeck, G., Scheidegger, C., Smith, B., and Venkatasubramanian, S · 2016
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
Cited alongside, same era.
Learning certifiably optimal rule lists
Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., and Rudin, C · 2017
Later among the works it cites.
A roadmap for a rigorous science of interpretability
Doshi-Velez, F. and Kim, B · 2017
Later among the works it cites.
Interpretable & explorable approximations of black box models
Lakkaraju, H., Kamar, E., Caruana, R., and Leskovec, J · 2017
Later among the works it cites.
Right for the right reasons: Training differentiable models by constraining their explanations
Ross, A. S., Hughes, M. C., and Doshi-Velez, F · 2017
Later among the works it cites.
Detecting statistical interactions from neural network weights
Tsang, M., Cheng, D., and Liu, Y · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Choi, E., Bahadori, M. T., Sun, J., Kulas, J., Schuetz, A., and Stewart, W · 2016
Cited alongside, same era.
Interpretable decision sets: A joint framework for description and prediction
Lakkaraju, H., Bach, S. H., and Leskovec, J · 2016
Cited alongside, same era.
Dealing with ambiguity–the ‘black box’as a design choice
Lissack, M · 2016
Cited alongside, same era.
General data protection regulation, 2016
Parliament and of the European Union, C · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Cited alongside, same era.
Supersparse linear integer models for optimized medical scoring systems
Ustun, B. and Rudin, C · 2016
Cited alongside, same era.
Later among the works it cites.
A bayesian framework for learning rule set for interpretable classification
Wang, T., Rudin, C., Doshi, F., Liu, Y., Klampfl, E., and MacNeille, P · 2017
Later among the works it cites.
Scalable bayesian rule lists
Yang, H., Rudin, C., and Seltzer, M · 2017
Later among the works it cites.
Interpretable classification models for recidivism prediction
Zeng, J., Ustun, B., and Rudin, C · 2017
Later among the works it cites.
Boolean decision rules via column generation
Dash, S., Gunluk, O., and Wei, D · 2018
Closest in time.
Multi-value rule sets for interpretable classification with feature-efficient representations
Wang, T · 2018
Closest in time.
Neural-symbolic vqa: Disentangling reasoning from vision and language understanding
Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., and Tenenbaum, J · 2018
Closest in time.
Hybrid predictive model: When an interpretable model collaborates with a black-box model
Wang, T. and Lin, Q · 2019
Closest in time.