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Complexity Bounds for Batch Active Learning in Classification

Philippe Rolet 1 Olivier Teytaud 1, 2
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Active learning is a branch of Machine Learning in which the learning algorithm, instead of being directly provided with pairs of problem instances and their solutions (their labels), is allowed to choose, from a set of unlabeled data, which instances to query. It is suited to settings where labeling instances is costly. This paper analyzes the speed-up of batch (parallel) active learning compared to sequential active learning (where instances are chosen 1 by 1): how faster can an algorithm become if it can query instances at once? There are two main contributions: proving lower and upper bounds on the possible gain, and illustrating them by experimenting on usual active learning algorithms. Roughly speaking, the speed-up is asymptotically logarithmic in the batch size (i.e. when ! 1). However, for some classes of functions with finite VC-dimension V , a linear speed-up can be achieved until a batch size of V . Practically speaking, this means that parallelizing computations on an expensive-to-label problem which is suited to active learning is very beneficial until V simultaneous queries, and less interesting (yet still bringing improvement) afterwards.
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Submitted on : Friday, November 5, 2010 - 4:40:00 PM
Last modification on : Thursday, July 8, 2021 - 3:48:47 AM
Long-term archiving on: : Sunday, February 6, 2011 - 3:00:42 AM


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  • HAL Id : inria-00533318, version 1



Philippe Rolet, Olivier Teytaud. Complexity Bounds for Batch Active Learning in Classification. ECML 2010, Oct 2010, Barcelone, Spain. ⟨inria-00533318⟩



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