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SoQal: Selective Oracle Questioning in Active Learning
Large sets of unlabelled data within the healthcare domain remain underu...
04/22/2020 ∙ by Dani Kiyasseh, et al. ∙
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The Label Complexity of Active Learning from Observational Data
Counterfactual learning from observational data involves learning a clas...
05/29/2019 ∙ by Songbai Yan, et al. ∙
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Learning in Confusion: Batch Active Learning with Noisy Oracle
We study the problem of training machine learning models incrementally u...
09/27/2019 ∙ by Gaurav Gupta, et al. ∙
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Active Learning under Label Shift
Distribution shift poses a challenge for active data collection in the r...
07/16/2020 ∙ by Eric Zhao, et al. ∙
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Exemplar Guided Active Learning
We consider the problem of wisely using a limited budget to label a smal...
11/02/2020 ∙ by Jason Hartford, et al. ∙
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The Power of Comparisons for Actively Learning Linear Classifiers
In the world of big data, large but costly to label datasets dominate ma...
07/08/2019 ∙ by Max Hopkins, et al. ∙
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Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization
The automatic detection of frauds in banking transactions has been recen...
11/20/2018 ∙ by Christelle Marfaing, et al. ∙
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Search Improves Label for Active Learning
02/23/2016 ∙ by Alina Beygelzimer, et al. ∙
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We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not). The Search oracle models the situation where a human searches a database to seed or counterexample an existing solution. Search is stronger than Label while being natural to implement in many situations. We show that an algorithm using both oracles can provide exponentially large problem-dependent improvements over Label alone.
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