On the sample complexity of adversarial multi-source PAC learning

N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, ArXiv (n.d.).

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Abstract
We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms. Specifically, we analyze the situation in which a learning system obtains datasets from multiple sources, some of which might be biased or even adversarially perturbed. It is known that in the single-source case, an adversary with the power to corrupt a fixed fraction of the training data can prevent PAC-learnability, that is, even in the limit of infinitely much training data, no learning system can approach the optimal test error. In this work we show that, surprisingly, the same is not true in the multi-source setting, where the adversary can arbitrarily corrupt a fixed fraction of the data sources. Our main results are a generalization bound that provides finite-sample guarantees for this learning setting, as well as corresponding lower bounds. Besides establishing PAC-learnability our results also show that in a cooperative learning setting sharing data with other parties has provable benefits, even if some participants are malicious.
Publishing Year
Date Published
2020-02-24
Journal Title
arXiv
Acknowledged SSUs
Article Number
2002.10384
Page
2002.10384
IST-REx-ID

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Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. arXiv.
Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (n.d.). On the sample complexity of adversarial multi-source PAC learning. arXiv.
Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” ArXiv, n.d.
N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” arXiv. .
Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. arXiv, 2002.10384.
Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” ArXiv, 2002.10384.

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