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Many machine learning algorithms require large numbers of labeled data to deliver state-of-the-art results.
Queries and concept learning
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Earlier work this paper cites.
Approximation of zonoids by zonotopes
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Earlier work this paper cites.
Query by committee
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Earlier work this paper cites.
A sequential algorithm for training text classifiers
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Randomized algorithms for matrices and data
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Earlier work this paper cites.
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Earlier work this paper cites.
Twice-ramanujan sparsifiers
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Earlier work this paper cites.
User-friendly tail bounds for sums of random matrices
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Earlier work this paper cites.
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