Fetching the paper…
Reading the bibliography…
Model distillation has been a popular method for producing interpretable machine learning.
Doob, J.L.: Application of the theory of martingales. Le calcul des probabilites et ses applications, 23–27 (1949)
1949
Earlier work this paper cites.
Donsker, M.D.: An Invariance Principle for Certain Probability Limit Theorems, (1951)
1951
Earlier work this paper cites.
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. brooks. Wadsworth and Brooks, Monterey, CA (1984)
1984
Earlier work this paper cites.
Quinlan, J.R.: Generating production rules from decision trees. In: Ijcai, vol. 87, pp. 304–307 (1987). Citeseer
1987
Earlier work this paper cites.
Koza, J.: On the programming of computers by means of natural selection. Genetic programming (1992)
1992
Earlier work this paper cites.
Wand, M.P., Jones, M.C.: Kernel Smoothing. CRC press, Boca Raton (1994)
1994
Earlier work this paper cites.
Bishop, C.M.: Mixture density networks (1994)
1994
Earlier work this paper cites.
Mangasarian, O.L., Street, W.N., Wolberg, W.H.: Breast cancer diagnosis and prognosis via linear programming. Operations Research 43
1995
Earlier work this paper cites.
Augusto, D.A., Barbosa, H.J.: Symbolic regression via genetic programming. In: Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks, pp. 173–178 (2000). IEEE
2000
Earlier work this paper cites.
Breiman, L.: Random forests. Machine learning 45
2001
Earlier work this paper cites.
Breiman, L.: Manual on setting up, using, and understanding random forests v3. 1. Statistics Department University of California Berkeley, CA, USA 1
2002
Earlier work this paper cites.
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. The Annals of statistics 32
2004
Earlier work this paper cites.
Elter, M., Schulz-Wendtland, R., Wittenberg, T.: The prediction of breast cancer biopsy outcomes using two cad approaches that both emphasize an intelligible decision process. Medical physics 34 11
2007
Earlier work this paper cites.
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., et al
2008
Cited alongside, same era.
Van Rossum, G., Drake, F.L.: Python 3 Reference Manual. CreateSpace, Scotts Valley (2009)
2009
Cited alongside, same era.
Duembgen, L.: Bounding standard gaussian tail probabilities. arXiv preprint arXiv:1012.2063 (2010)
2010
Cited alongside, same era.
Johansson, U., Sönströd, C., Löfström, T.: One tree to explain them all. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 1444–1451 (2011). IEEE
2011
Cited alongside, same era.
Loh, W.-Y.: Classification and regression trees. Wiley interdisciplinary reviews: data mining and knowledge discovery 1
2011
Cited alongside, same era.
Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. Advances in neural information processing systems 30
2017
Later among the works it cites.
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Routledge, Oxfordshire (2017)
2017
Later among the works it cites.
Meurer, A., Smith, C.P., Paprocki, M., Čertík, O., Kirpichev, S.B., Rocklin, M., Kumar, A., Ivanov, S., Moore, J.K., Singh, S., et al
2017
Later among the works it cites.
Tan, S., Caruana, R., Hooker, G., Lou, Y.: Distill-and-compare: Auditing black-box models using transparent model distillation. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 303–310 (2018)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12
2011
Cited alongside, same era.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
2013
Cited alongside, same era.
Kuhn, M., Johnson, K., et al
2013
Cited alongside, same era.
Quinlan, J.R.: C4. 5: Programs for Machine Learning. Elsevier, Amsterdam (2014)
2014
Cited alongside, same era.
Affenzeller, M., Winkler, S.M., Kronberger, G., Kommenda, M., Burlacu, B., Wagner, S.: Gaining deeper insights in symbolic regression. In: Genetic Programming Theory and Practice XI, pp. 175–190. Springer, Berlin (2014)
2014
Cited alongside, same era.
Wang, F., Rudin, C.: Falling rule lists. In: Artificial Intelligence and Statistics, pp. 1013–1022 (2015). PMLR
2015
Cited alongside, same era.
Ribeiro, M.T., Singh, S., Guestrin, C.: ” why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
2016
Cited alongside, same era.
2018
Later among the works it cites.
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: An overview. IEEE signal processing magazine 35
2018
Later among the works it cites.
Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning. Communications of the ACM 63
2019
Later among the works it cites.
Ferreira, L.A., Guimarães, F.G., Silva, R.: Applying genetic programming to improve interpretability in machine learning models. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2020)
2020
Later among the works it cites.
Zhou, Z., Hooker, G., Wang, F.: S-lime: Stabilized-lime for model explanation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2429–2438 (2021)
2021
Later among the works it cites.
Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M.G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., et al
2021
Later among the works it cites.
Aldeia, G.S.I., França, F.O.: Interpretability in symbolic regression: a benchmark of explanatory methods using the feynman data set. Genetic Programming and Evolvable Machines, 1–41 (2022)
2022
Closest in time.