The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On the learnability of discrete distributions
Open accessAbstract
We introduce and investigate a new model of learning probability distributions from independent draws. Our model is inspired by the popular Probably Approximately Correct (PAC) model for learning boolean functions from labeled
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Cites
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
Self-citation
Cited by
Property testing and its connection to learning and approximation
Goldreich, O., Goldwasser, S., & Ron, D. (1998) · Journal of the ACM · doi:10.1145/285055.285060 · Cited by 1,020
Self-citationA spectral algorithm for learning Hidden Markov Models
Hsu, D., Kakade, S. M., & Zhang, T. (2012) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2011.12.025 · Cited by 278
The Born supremacy: quantum advantage and training of an Ising Born machine
Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020) · npj Quantum Information · doi:10.1038/s41534-020-00288-9 · Cited by 155
Efficient PAC Reinforcement Learning in Regular Decision Processes
Ronca, A., & De Giacomo, G. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/279 · Cited by 6
Metadata
- Authors
- Kearns, M., Mansour, Y., Ron, D., Rubinfeld, R., Schapire, R. E., & Sellie, L.
- Venue
- Proceedings of the twenty-sixth annual ACM symposium on Theory of computing - STOC '94
- Publisher
- ACM Press
- Published
- 1994
- Pages
- 273-282
- OpenAlex
- W2095374884
- Cited by
- 273
- References
- 1
- FWCI
- 6.24
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: On the learnability of discrete distributions. https://app.cochat.ai/papers/10.1145/195058.195155
- The paper
- Kearns, M., Mansour, Y., Ron, D., Rubinfeld, R., Schapire, R. E., & Sellie, L. (1994). On the learnability of discrete distributions. Proceedings of the twenty-sixth annual ACM symposium on Theory of computing - STOC '94. https://doi.org/10.1145/195058.195155
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
