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Machine Learning
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Active Learning
1885 directly classified papers
Papers per year
2001: 1
2002: 1
2004: 2
2005: 4
2006: 8
2007: 12
2008: 8
2009: 11
2010: 20
2011: 26
2012: 38
2013: 50
2014: 46
2015: 39
2016: 45
2017: 76
2018: 102
2019: 160
2020: 152
2021: 232
2022: 215
2023: 252
2024: 222
2025: 119
2026: 44
Papers
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
ICML 2019
A Bayesian Approach for Sequence Tagging with Crowds
EMNLP 2019
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
CORL 2019
Adaptive Influence Maximization with Myopic Feedback
NIPS 2019
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
NIPS 2019
Learning Nearest Neighbor Graphs from Noisy Distance Samples
NIPS 2019
Learning Deterministic Weighted Automata with Queries and Counterexamples
NIPS 2019
Learning to Caption Images Through a Lifetime by Asking Questions
ICCV 2019
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts
IJCNLP 2019
Learning to Ask for Conversational Machine Learning
IJCNLP 2019
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation
IJCNLP 2019
Sampling Bias in Deep Active Classification: An Empirical Study
IJCNLP 2019
Practical Obstacles to Deploying Active Learning
IJCNLP 2019
TableSense: Spreadsheet Table Detection with Convolutional Neural Networks
AAAI 2019
Biomedical Image Segmentation via Representative Annotation
AAAI 2019
Active Learning for New Domains in Natural Language Understanding
NAACL 2019
Data-efficient Neural Text Compression with Interactive Learning
NAACL 2019
Adaptive Ensemble Active Learning for Drifting Data Stream Mining
IJCAI 2019
Active Learning of Multi-Class Classification Models from Ordered Class Sets
AAAI 2019
Rapid Performance Gain through Active Model Reuse
IJCAI 2019
Thompson Sampling on Symmetric Alpha-Stable Bandits
IJCAI 2019
Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach
AAAI 2019
Active Sampling for Open-Set Classification without Initial Annotation
AAAI 2019
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers
EMNLP 2019
Bidirectional Active Learning with Gold-Instance-Based Human Training
IJCAI 2019
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