Contreras-Ochando, L., Ferri, C., & Hernández-Orallo, J. (2020a). Automating common data science matrix transformations. In Machine learning and knowledge discovery in databases (ECMLPKDD workshop on automating data science) (pp. 17–27). Springer, ECML-PKDD ’19.
Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Martínez-Plumed, F., Ramírez-Quintana, M. J., & Katayama, S. (2020b). Automated data transformation with inductive programming and dynamic background knowledge. In Machine learning and knowledge discovery in databases (pp. 735–751). Springer, ECML-PKDD ’19.
Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Martínez-Plumed, F., Ramírez-Quintana, M. J., & Katayama, S. (2020c). BK-ADAPT: Dynamic background knowledge for automating data transformation. In Machine learning and knowledge discovery in databases (ECMLPKDD demo track) (pp. 755–759). Springer, ECML-PKDD ’19.
[+]
Contreras-Ochando, L., Ferri, C., & Hernández-Orallo, J. (2020a). Automating common data science matrix transformations. In Machine learning and knowledge discovery in databases (ECMLPKDD workshop on automating data science) (pp. 17–27). Springer, ECML-PKDD ’19.
Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Martínez-Plumed, F., Ramírez-Quintana, M. J., & Katayama, S. (2020b). Automated data transformation with inductive programming and dynamic background knowledge. In Machine learning and knowledge discovery in databases (pp. 735–751). Springer, ECML-PKDD ’19.
Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Martínez-Plumed, F., Ramírez-Quintana, M. J., & Katayama, S. (2020c). BK-ADAPT: Dynamic background knowledge for automating data transformation. In Machine learning and knowledge discovery in databases (ECMLPKDD demo track) (pp. 755–759). Springer, ECML-PKDD ’19.
Cropper, A., Tamaddoni, A., & Muggleton, S. H. (2015). Meta-interpretive learning of data transformation programs. In Inductive logic programming (pp. 46–59).
Ferri-Ramírez, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2001). Incremental learning of functional logic programs. In FLOPS (pp. 233–247). Springer.
Gulwani, S. (2011). Automating string processing in spreadsheets using input-output examples. In Proceedings 38th principles of programming languages (pp. 317–330).
Gulwani, S., Hernández-Orallo, J., Kitzelmann, E., Muggleton, S., Schmid, U., & Zorn, B. (2015). Inductive programming meets the real world. Communications of the ACM, 58(11), 90–99.
He, Y., Chu, X., Ganjam, K., Zheng, Y., Narasayya, V., & Chaudhuri, S. (2018). Transform-data-by-example (TDE): An extensible search engine for data transformations. Proceedings of the VLDB Endowment, 11(10), 1165–1177.
Jenkins, T. (2002). On the difficulty of learning to program. In Proceedings of the 3rd annual conference of the LTSN Centre for information and computer sciences, Citeseer (Vol. 4, pp. 53–58).
Kandel, S., Paepcke, A., Hellerstein, J., & Heer, J. (2011). Wrangler: Interactive visual specification of data transformation scripts. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 3363–3372). ACM.
Katayama, S. (2005). Systematic search for lambda expressions. Trends in Functional Programming, 6, 111–126.
Kolb, S., Paramonov, S., Guns, T., & De Raedt, L. (2017). Learning constraints in spreadsheets and tabular data. Machine Learning, 106(9–10), 1441–1468.
Lieberman, H. (2001). Your wish is my command: Programming by example. Burlington: Morgan Kaufmann.
Menon, A., Tamuz, O., Gulwani, S., Lampson, B., & Kalai, A. (2013). A machine learning framework for programming by example. In ICML (pp. 187–195).
Mitchell, T., Allen, J., Chalasani, P., Cheng, J., Etzioni, O., Ringuette, M., & Schlimmer, J. (1991). Theo: A framework for self-improving systems. In Architectures for intelligence (pp. 323–355).
Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Yang, B., Betteridge, J., et al. (2018). Never-ending learning. Communications of the ACM, 61(5), 103–115.
Paramonov, S., Kolb, S., Guns, T., & De Raedt, L. (2017). Tacle: Learning constraints in tabular data. In Proceedings of the 2017 ACM on conference on information and knowledge management, ACM, New York, NY, USA, CIKM ’17 (pp. 2511–2514).
Parisotto, E., Mohamed, Ar., Singh, R., Li, L., Zhou, D., & Kohli, P. (2016). Neuro-symbolic program synthesis. arXiv preprint arXiv:161101855
Raza, M., Gulwani, S., & Milic-Frayling, N. (2014). Programming by example using least general generalizations. In Twenty-eighth AAAI conference on artificial intelligence.
Reynolds, A., & Tinelli, C. (2017). Sygus techniques in the core of an SMT solver. arXiv preprint arXiv:171110641
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Inf Process Manag, 24(5), 513–523.
Santolucito, M., Hallahan, W. T., & Piskac, R. (2019). Live programming by example. In Extended abstracts of the 2019 CHI conference on human factors in computing systems (p. INT020). ACM.
Segovia-Aguas, J., Jiménez, S., & Jonsson, A. (2019). Computing programs for generalized planning using a classical planner. Artificial Intelligence, 272, 52–85.
Wang, X., Dillig, I., & Singh, R. (2017). Program synthesis using abstraction refinement. In Proceedings of the ACM on programming languages 2(POPL):63.
Wu, B., Szekely, P., & Knoblock, C. A. (2012). Learning data transformation rules through examples: Preliminary results. In Information integration on the web (p. 8).
[-]