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SoQal: Selective Oracle Questioning in Active Learning
Large sets of unlabelled data within the healthcare domain remain underu...
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The Label Complexity of Active Learning from Observational Data
Counterfactual learning from observational data involves learning a clas...
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Learning in Confusion: Batch Active Learning with Noisy Oracle
We study the problem of training machine learning models incrementally u...
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Active Learning under Label Shift
Distribution shift poses a challenge for active data collection in the r...
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Exemplar Guided Active Learning
We consider the problem of wisely using a limited budget to label a smal...
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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...
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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...
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Search Improves Label for Active Learning
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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