A Markovian Formalism for Active Querying
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Abstract (arXiv)
Active learning algorithms have been an integral part of recent advances in artificial intelligence. However, the research in the field is widely varying and lacks an overall organizing leans. We outline a Markovian formalism for the field of active learning and survey the literature to demonstrate the organizing capability of our proposed formalism. Our formalism takes a partially observable Markovian system approach to the active learning process as a whole. We specifically outline how querying, dataset augmentation, reward updates, and other aspects of active learning can be viewed as a transition between meta-states in a Markovian system, and give direction into how other aspects of active learning can fit into our formalism.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 13 Jun 2023.
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17 of 33 references.
References · 17
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2020Learning to summarize from human feedbackcited by 2,588
- 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisonscited by 247
- 2017Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentationcited by 146
- 2021Batch Active Learning at Scalecited by 127
- 2021Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraintscited by 124
- 2020Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferencescited by 71
- 2019Asking Easy Questions: A User-Friendly Approach to Active Reward Learningcited by 68
- 2019Learning Reward Functions by Integrating Human Demonstrations and Preferencescited by 64
- 2016Coresets and Sketchescited by 63
- 2017Active classification with comparison queriescited by 27
- 2019The Power of Comparisons for Actively Learning Linear Classifierscited by 20
5 more
- 2018Active Deep Q-learning with Demonstrationcited by 10
- 2021Active Learning of Driving Scenario Trajectoriescited by 7
- 2021Reinforcement Learning with Feedback from Multiple Humans with Diverse Skillscited by 6
- 2022Active Learning with Label Comparisonscited by 5
- 2022Entity Matching by Pool-based Active Learningcited by 1
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