eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2018-01-12
47:1
47:20
10.4230/LIPIcs.ITCS.2018.47
article
Learning Discrete Distributions from Untrusted Batches
Qiao, Mingda
Valiant, Gregory
We consider the problem of learning a discrete distribution in the presence of an epsilon fraction of malicious data sources. Specifically, we consider the setting where there is some underlying distribution, p, and each data source provides a batch of >= k samples, with the guarantee that at least a (1 - epsilon) fraction of the sources draw their samples from a distribution with total variation distance at most \eta from p. We make no assumptions on the data provided by the remaining epsilon fraction of sources--this data can even be chosen as an adversarial function of the (1 - epsilon) fraction of "good" batches. We provide two algorithms: one with runtime exponential in the support size, n, but polynomial in k, 1/epsilon and 1/eta that takes O((n + k)/epsilon^2) batches and recovers p to error O(eta + epsilon/sqrt(k)). This recovery accuracy is information theoretically optimal, to constant factors, even given an infinite number of data sources. Our second algorithm applies to the eta = 0 setting and also achieves an O(epsilon/sqrt(k)) recover guarantee, though it runs in poly((nk)^k) time. This second algorithm, which approximates a certain tensor via a rank-1 tensor minimizing l_1 distance, is surprising in light of the hardness of many low-rank tensor approximation problems, and may be of independent interest.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol094-itcs2018/LIPIcs.ITCS.2018.47/LIPIcs.ITCS.2018.47.pdf
robust statistics
information-theoretic optimality