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  • Charikar M and Pabbaraju C. A Characterization of List Learnability. Proceedings of the 55th Annual ACM Symposium on Theory of Computing. (1713-1726).

    https://doi.org/10.1145/3564246.3585190

  • Michael L. Introspective forecasting. Proceedings of the 24th International Conference on Artificial Intelligence. (3714-3720).

    /doi/10.5555/2832747.2832767

  • Michael L. Learning from partial observations. Proceedings of the 20th international joint conference on Artifical intelligence. (968-974).

    /doi/10.5555/1625275.1625432

  • Arpe J and Reischuk R. Learning juntas in the presence of noise. Proceedings of the Third international conference on Theory and Applications of Models of Computation. (387-398).

    https://doi.org/10.1007/11750321_37

  • Zhang B and Jang H. Molecular learning of wDNF formulae. Proceedings of the 11th international conference on DNA Computing. (427-437).

    https://doi.org/10.1007/11753681_34

  • Bshouty N and Feldman V. (2002). On using extended statistical queries to avoid membership queries. The Journal of Machine Learning Research. 2. (359-395). Online publication date: 1-Mar-2002.

    https://doi.org/10.1162/153244302760200669

  • Bshouty N, Jackson J and Tamon C. Uniform-distribution attribute noise learnability. Proceedings of the twelfth annual conference on Computational learning theory. (75-80).

    https://doi.org/10.1145/307400.307414

  • Gentile C and Helmbold D. Improved lower bounds for learning from noisy examples. Proceedings of the eleventh annual conference on Computational learning theory. (104-115).

    https://doi.org/10.1145/279943.279965

  • Goldman S, Kwek S and Scott S. Learning from examples with unspecified attribute values (extended abstract). Proceedings of the tenth annual conference on Computational learning theory. (231-242).

    https://doi.org/10.1145/267460.267504

  • Frazier M, Goldman S, Mishra N and Pitt L. (1996). Learning from a Consistently Ignorant Teacher. Journal of Computer and System Sciences. 52:3. (471-492). Online publication date: 1-Jun-1996.

    https://doi.org/10.1006/jcss.1996.0035

  • Decatur S and Gennaro R. On learning from noisy and incomplete examples. Proceedings of the eighth annual conference on Computational learning theory. (353-360).

    https://doi.org/10.1145/225298.225341

  • Blum A, Chalasani P, Goldman S and Slonim D. Learning with unreliable boundary queries. Proceedings of the eighth annual conference on Computational learning theory. (98-107).

    https://doi.org/10.1145/225298.225310

  • Goldman S and Sloan R. (1995). Can PAC learning algorithms tolerate random attribute noise?. Algorithmica. 14:1. (70-84). Online publication date: 1-Jul-1995.

    https://doi.org/10.1007/BF01300374

  • Frazier M, Goldman S, Mishra N and Pitt L. Learning from a consistently ignorant teacher. Proceedings of the seventh annual conference on Computational learning theory. (328-339).

    https://doi.org/10.1145/180139.181170

  • Ron D and Rubinfeld R. Learning fallible finite state automata. Proceedings of the sixth annual conference on Computational learning theory. (218-227).

    https://doi.org/10.1145/168304.168336

  • Angluin D. Computational learning theory. Proceedings of the twenty-fourth annual ACM symposium on Theory of Computing. (351-369).

    https://doi.org/10.1145/129712.129746