Fetching the paper…
Reading the bibliography…
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine.
arXiv preprint arXiv:1905.04610
Lundberg SM, et al. (2019) Explainable ai for trees: From local explanations to global understanding · 1905
Earlier work this paper cites.
arXiv preprint arXiv:1905.07631
Devlin S, Singh C, Murdoch WJ, Yu B (2019) Disentangled attribution curves for interpreting random forests and boosted trees · 1905
Earlier work this paper cites.
The Lancet
Bennett P, Burch T, Miller M (1971) Diabetes mellitus in american (pima) indians · 1971
Earlier work this paper cites.
Siam journal on applied mathematics
Meyer, Jr CD (1973) Generalized inversion of modified matrices · 1973
Earlier work this paper cites.
The annals of statistics
Schwarz G (1978) Estimating the dimension of a model · 1978
Earlier work this paper cites.
(Chapman and Hall/CRC)
Breiman L, Friedman J, Olshen R, Stone CJ (1984) Classification and regression trees · 1984
Earlier work this paper cites.
Journal of the American statistical Association
Breiman L, Friedman JH (1985) Estimating optimal transformations for multiple regression and correlation · 1985
Earlier work this paper cites.
Machine learning
Quinlan JR (1986) Induction of decision trees · 1986
Earlier work this paper cites.
(American Medical Informatics Association), p. 261
Smith JW, Everhart JE, Dickson W, Knowler WC, Johannes RS (1988) Using the adap learning algorithm to forecast the onset of diabetes mellitus in Proceedings of the annual symposium on computer application in medical care · 1988
Earlier work this paper cites.
Machine learning
Pagallo G, Haussler D (1990) Boolean feature discovery in empirical learning · 1990
Earlier work this paper cites.
The annals of statistics
Friedman JH (1991) Multivariate adaptive regression splines · 1991
Earlier work this paper cites.
Sea Fisheries Division, Technical Report
Nash WJ, Sellers TL, Talbot SR, Cawthorn AJ, Ford WB (1994) The population biology of abalone (haliotis species) in tasmania. i. blacklip abalone (h. rubra) from the north coast and islands of bass strait · 1994
Earlier work this paper cites.
Carnegie Corporation of New York Task Force on the Needs of Young Children; An earlier version of this article was presented as a position paper for the aforementioned corporation
Osofsky JD (1997) The effects of exposure to violence on young children (1995) · 1995
Earlier work this paper cites.
(Citeseer), Vol. 96, pp. 148–156
Freund Y, Schapire RE, , et al. (1996) Experiments with a new boosting algorithm in icml · 1996
Earlier work this paper cites.
Machine learning
Breiman L (1996) Bagging predictors · 1996
Earlier work this paper cites.
Statistics & Probability Letters
Pace RK, Barry R (1997) Sparse spatial autoregressions · 1997
Earlier work this paper cites.
AAAI/IAAI
Cohen WW, Singer Y (1999) A simple, fast, and effective rule learner · 1999
Earlier work this paper cites.
Machine learning
Breiman L (2001) Random forests · 2001
Earlier work this paper cites.
Annals of statistics
Friedman JH (2001) Greedy function approximation: a gradient boosting machine · 2001
Earlier work this paper cites.
The Lancet
Stiell IG, et al. (2001) The canadian ct head rule for patients with minor head injury · 2001
Earlier work this paper cites.
Annals of emergency medicine
Holmes JF, et al. (2002) Identification of children with intra-abdominal injuries after blunt trauma · 2002
Earlier work this paper cites.
The annals of Statistics
Bühlmann P, Yu B (2002) Analyzing bagging · 2002
Earlier work this paper cites.
The Annals of statistics
Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression · 2004
Cited alongside, same era.
Asuncion A, Newman D (2007) Uci machine learning repository
2007
Cited alongside, same era.
pp. 224–231
Dembczyński K, Kotłowski W, Słowiński R (2008) Maximum likelihood rule ensembles in Proceedings of the 25th international conference on Machine learning · 2008
Cited alongside, same era.
The Annals of Applied Statistics
Friedman JH, Popescu BE, , et al. (2008) Predictive learning via rule ensembles · 2008
Cited alongside, same era.
Expert Systems with Applications
Yeh IC, Lien Ch (2009) The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients · 2009
Cited alongside, same era.
The Lancet
Kuppermann N, et al. (2009) Identification of children at very low risk of clinically-important brain injuries after head trauma: a prospective cohort study · 2009
Cited alongside, same era.
arXiv preprint arXiv:1810.07287
Kumbier K, Basu S, Brown JB, Celniker S, Yu B (2018) Refining interaction search through signed iterative random forests · 2018
Later among the works it cites.
Nature Machine Intelligence
Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead · 2019
Later among the works it cites.
Proceedings of the National Academy of Sciences
Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B (2019) Definitions, methods, and applications in interpretable machine learning · 2019
Later among the works it cites.
Advances in Neural Information Processing Systems (NeurIPS)
Hu X, Rudin C, Seltzer M (2019) Optimal sparse decision trees · 2019
Later among the works it cites.
Proceedings of the national academy of sciences
Luna JM, et al. (2019) Building more accurate decision trees with the additive tree · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The Annals of Applied Statistics
Chipman HA, George EI, McCulloch RE (2010) Bart: Bayesian additive regression trees · 2010
Cited alongside, same era.
arXiv preprint arXiv:2012.00058
Romano JD, et al. (2020) Pmlb v1. 0: an open source dataset collection for benchmarking machine learning methods · 2012
Cited alongside, same era.
Journal of Machine Learning Research
Raskutti G, J Wainwright M, Yu B (2012) Minimax-optimal rates for sparse additive models over kernel classes via convex programming · 2012
Cited alongside, same era.
Proceedings of the National Academy of Sciences
Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: Genetic interactions create phantom heritability · 2012
Cited alongside, same era.
Bernoulli
Yu B (2013) Stability · 2013
Cited alongside, same era.
Annals of emergency medicine
Holmes JF, Lillis K, Monroe, David Borgialli D, Kerrey BT, , et al. (2013) Identifying children at very low risk of clinically important blunt abdominal injuries · 2013
Cited alongside, same era.
Wang T (2019) Gaining free or low-cost interpretability with interpretable partial substitute in Proceedings of the 36th International Conference on Machine Learning · 2019
Later among the works it cites.
Journal of Pediatric Surgery
Bertsimas D, Masiakos PT, Mylonas KS, Wiberg H (2019) Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach · 2019
Later among the works it cites.
Pediatrics
Leonard JC, et al. (2019) Cervical spine injury risk factors in children with blunt trauma · 2019
Later among the works it cites.
(Lulu. com)
Molnar C (2020) Interpretable machine learning · 2020
Later among the works it cites.
Proceedings of the National Academy of Sciences
Yu B, Kumbier K (2020) Veridical data science · 2020
Later among the works it cites.
(PMLR), pp. 6150–6160
Lin J, Zhong C, Hu D, Rudin C, Seltzer M (2020) Generalized and scalable optimal sparse decision trees in International Conference on Machine Learning · 2020
Later among the works it cites.
The Journal of Machine Learning Research
Mentch L, Zhou S (2020) Randomization as regularization: A degrees of freedom explanation for random forest success · 2020
Later among the works it cites.
(PMLR), pp. 3525–3535
LeJeune D, Javadi H, Baraniuk R (2020) The implicit regularization of ordinary least squares ensembles in International Conference on Artificial Intelligence and Statistics · 2020
Later among the works it cites.
Journal of Open Source Software
Singh C, Nasseri K, Tan YS, Tang T, Yu B (2021) imodels: a python package for fitting interpretable models · 2021
Later among the works it cites.
arXiv preprint arXiv:2103.11251
Rudin C, et al. (2021) Interpretable machine learning: Fundamental principles and 10 grand challenges · 2021
Later among the works it cites.
Advances in Neural Information Processing Systems
Ha W, Singh C, Lanusse F, Upadhyayula S, Yu B (2021) Adaptive wavelet distillation from neural networks through interpretations · 2021
Later among the works it cites.
arXiv preprint arXiv:2110.09626
Tan YS, Agarwal A, Yu B (2021) A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds · 2021
Later among the works it cites.
arXiv preprint arXiv:2102.11800
Behr M, Wang Y, Li X, Yu B (2021) Provable boolean interaction recovery from tree ensemble obtained via random forests · 2021
Later among the works it cites.
arXiv preprint arXiv:2104.13881
Klusowski JM (2021) Universal consistency of decision trees in high dimensions · 2021
Later among the works it cites.
Nasseri K, Singh C, Duncan J, Kornblith A, Yu B (2022) Group probability-weighted tree sums for interpretable modeling of heterogeneous data
2022
Closest in time.
arXiv preprint arXiv:2202.00858
Agarwal A, Tan YS, Ronen O, Singh C, Yu B (2022) Hierarchical shrinkage: improving the accuracy and interpretability of tree-based methods · 2022
Closest in time.
Agarwal A, Kenney AM, Tan YS, Tang TM, Yu B (2023) Mdi+: A flexible random forest-based feature importance framework
2023
Closest in time.