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Local Interpretable Model-Agnostic Explanations (LIME) is a popular method to perform interpretability of any kind of Machine Learning (ML) model.
Ridge Regression: Biased Estimation for Nonorthogonal Problems,
A. E. Hoerl, R. W. Kennard, · 1970
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C. S. Cox, Plan and Operation of the NHANES I Epidemiologic Followup Study, 1987, 27, US Department of Health and Human Services, Public Health Service, Centers …, 1992
1992
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Body mass index, weight change, and risk of mobility disability in middle-aged and older women: The epidemiologic follow-up study of NHANES I,
L. J. Launer, T. Harris, C. Rumpel, J. Madans, · 1994
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Extracting tree-structured representations of trained networks,
M. Craven, J. W. Shavlik, · 1996
Earlier work this paper cites.
Serum uric acid and cardiovascular mortality: The NHANES I epidemiologic follow-up study, 1971-1992,
J. Fang, M. H. Alderman, · 2000
Earlier work this paper cites.
Time-dependent ROC curves for censored survival data and a diagnostic marker,
P. J. Heagerty, T. Lumley, M. S. Pepe, · 2000
Earlier work this paper cites.
Machine learning for medical diagnosis: History, state of the art and perspective,
I. Kononenko, · 2001
Earlier work this paper cites.
Random forests,
L. Breiman, · 2001
Earlier work this paper cites.
Greedy function approximation: A gradient boosting machine,
J. H. Friedman, · 2001
Earlier work this paper cites.
W. H. Greene, Econometric Analysis, Pearson Education India, 2003
2003
Earlier work this paper cites.
Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data,
T. A. Lasko, J. C. Denny, M. A. Levy, · 2013
Earlier work this paper cites.
Deep computational phenotyping,
Z. Che, D. Kale, W. Li, M. T. Bahadori, Y. Liu, · 2015
Earlier work this paper cites.
Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation,
A. Goldstein, A. Kapelner, J. Bleich, E. Pitkin, · 2015
Earlier work this paper cites.
Deep patient: An unsupervised representation to predict the future of patients from the electronic health records,
R. Miotto, L. Li, B. A. Kidd, J. T. Dudley, · 2016
Earlier work this paper cites.
Doctor ai: Predicting clinical events via recurrent neural networks,
E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart, J. Sun, · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier,
M. T. Ribeiro, S. Singh, C. Guestrin, · 2016
Cited alongside, same era.
Machine learning model interpretability for precision medicine,
G. J. Katuwal, R. Chen, · 2016
Cited alongside, same era.
Visualizing the effects of predictor variables in black box supervised learning models,
D. W. Apley, J. Zhu, · 2016
Cited alongside, same era.
Interpreting models via single tree approximation,
Y. Zhou, G. Hooker, · 2016
Cited alongside, same era.
On the robustness of interpretability methods,
D. Alvarez-Melis, T. S. Jaakkola, · 2018
Later among the works it cites.
Causability and explainability of artificial intelligence in medicine,
A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller, · 2019
Later among the works it cites.
High-performance medicine: The convergence of human and artificial intelligence,
E. J. Topol, · 2019
Later among the works it cites.
IBreakDown: Uncertainty of model explanations for non-additive predictive models,
A. Gosiewska, P. Biecek, · 2019
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M. R. Zafar, N. M. Khan, · 2019
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Xgboost: A scalable tree boosting system,
T. Chen, C. Guestrin, · 2016
Cited alongside, same era.
Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis,
B. Shickel, P. J. Tighe, A. Bihorac, P. Rashidi, · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions,
S. M. Lundberg, S.-I. Lee, · 2017
Cited alongside, same era.
Scalable and accurate deep learning with electronic health records,
A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. Liu, J. Marcus, M. Sun, · 2018
Cited alongside, same era.
From machine learning to explainable AI,
A. Holzinger, · 2018
Cited alongside, same era.
A survey of methods for explaining black box models,
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, D. Pedreschi, · 2018
Cited alongside, same era.
Development of a Radiology Decision Support System for the Classification of MRI Brain Scans,
A. Y. Zhang, S. S. W. Lam, N. Liu, Y. Pang, L. L. Chan, P. H. Tang, · 2018
Cited alongside, same era.
ALIME: Autoencoder Based Approach for Local Interpretability,
S. M. Shankaranarayana, D. Runje, · 2019
Later among the works it cites.
Lecture notes on ridge regression,
W. N. van Wieringen, · 2019
Later among the works it cites.
Constrained Bayesian optimization with noisy experiments,
B. Letham, B. Karrer, G. Ottoni, E. Bakshy, · 2019
Later among the works it cites.
C. Molnar, Interpretable Machine Learning, Lulu. com, 2020
2020
Closest in time.
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics,
C. Moreira, R. Sindhgatta, C. Ouyang, P. Bruza, A. Wichert, · 2020
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Statistical stability indices for LIME: Obtaining reliable explanations for Machine Learning models,
G. Visani, E. Bagli, F. Chesani, A. Poluzzi, D. Capuzzo, · 2020
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C. Molnar, Limitations of Interpretable Machine Learning Methods, 2020
2020
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From local explanations to global understanding with explainable AI for trees,
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, S.-I. Lee, · 2020
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