- EOAL: Entropic Open-set Active Learning [2024, AAAI]
- EASAL: Entity-Aware Subsequence-Based Active Learning for Named Entity Recognition [2023, AAAI]
- Multi-Classifier Adversarial Optimization for Active Learning [2023, AAAI]
- Beam Search Optimized Batch Bayesian Active Learning [2023, AAAI]
- Boosting Active Learning via Improving Test Performance [2022, AAAI]
- Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries [2021, AAAI]:
- Active Ordinal Querying for Tuplewise Similarity Learning [2020 AAAI]
- Cost-Accuracy Aware Adaptive Labeling for Active Learning [2020, AAAI]: Need to select instances and labelers.
- Interactive Learning with Proactive Cognition Enhancement for Crowd Workers [2020, AAAI]: Try to help workers improve their reliability. Add a machine teaching part. Generate exemplars for human learners with the help of the ground truth inference algorithms.
- Self-Paced Active Learning: Query the Right Thing at the Right Time [AAAI 2019]
- An Interactive Multi-Label Consensus Labeling Model for Multiple Labeler Judgments [2018, AAAI]: The premise is that labels inferred with high consensus among labelers, might be closer to the ground truth. Proposed a novel formulation that aims to collectively optimize the cost of labeling, labeler reliability, label-label correlation and inter-labeler consensus.
- Cost-effective active learning from diverse labelers [2017, AAAI]: The cost of a labeler is proportional to its overall labeling quality. But different labelers usually have diverse expertise, and thus it is likely that labelers with a low overall quality can provide accurate labels on some specific instances. Select labeler can provide an accurate label for the instance with a relative low cost.
- Active Discriminative Text Representation Learning [AAAI, 2017]
- Active learning with cross-class similarity transfer [2017, AAAI]
- Online Active Linear Regression via Thresholding [2017, AAAI]
- Active learning with cross-class knowledge transfer [2016, AAAI]
- Multi-domain active learning for recommendation [2016, AAAI]
- Active learning by learning [2015, AAAI]
- Multi-Label Active Learning: Query Type Matters [2015, IJCAI]
- Active learning with model selection [2014, AAAI]
- Active learning with multi-label svm classification [2013, IJCAI]
- Multi-Task Active Learning with Output Constraints [2010, AAAI]
- Incorporating diversity in active learning with support vector machines [2003, ICML]