Active Learning

A Survey of Active Learning for Natural Language Processing EMNLP 2022 Asymptotic optimality for active learning processes UAI 2022 Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits AISTATS 2022 A Word-and-Paradigm Workflow for Fieldwork Annotation ACL 2022 Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning AAAI 2022 Batch Multi-Fidelity Active Learning with Budget Constraints NeurIPS 2022 Bayesian Active Learning with Fully Bayesian Gaussian Processes NeurIPS 2022 Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides of the Same Coin? WACV 2022 Best Arm Identification with Safety Constraints AISTATS 2022 Bigger Data or Fairer Data? Augmenting BERT via Active Sampling for Educational Text Classification COLING 2022 Biological Sequence Design with GFlowNets ICML 2022 Boosting Active Learning via Improving Test Performance AAAI 2022 BoostMIS: Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation CVPR 2022 Chernoff Sampling for Active Testing and Extension to Active Regression AISTATS 2022 CHIA: CHoosing Instances to Annotate for Machine Translation EMNLP 2022 Class-Balanced Active Learning for Image Classification WACV 2022 Clean or Annotate: How to Spend a Limited Data Collection Budget NAACL 2022 Clustering with Queries under Semi-Random Noise COLT 2022 Coherence-based Label Propagation over Time Series for Accelerated Active Learning ICLR 2022 COMET-QE and Active Learning for Low-Resource Machine Translation EMNLP 2022 Consistency Driven Sequential Transformers Attention Model for Partially Observable Scenes CVPR 2022 Constants Matter: The Performance Gains of Active Learning ICML 2022 Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling EMNLP 2022 Convergence of Uncertainty Sampling for Active Learning ICML 2022 CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation AAAI 2022