Active Learning

AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection AAAI 2020 Batch Decorrelation for Active Metric Learning IJCAI 2020 Best-item Learning in Random Utility Models with Subset Choices AISTATS 2020 Beyond Labels: Knowledge Elicitation using Deep Metric Learning and Psychometric Testing IJCAI 2020 Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles ICML 2020 BINOCULARS for efficient, nonmyopic sequential experimental design ICML 2020 Boosting-Based Reliable Model Reuse ACML 2020 Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems AAAI 2020 Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning ACL 2020 CheXpert++: Approximating the CheXpert Labeler for Speed, Differentiability, and Probabilistic Output MLHC 2020 Class Prior Estimation in Active Positive and Unlabeled Learning IJCAI 2020 Cold-start Active Learning through Self-supervised Language Modeling EMNLP 2020 Color Composition Similarity and Its Application in Fine-grained Similarity WACV 2020 Competing Bandits in Matching Markets AISTATS 2020 Confidence-Aware Learning for Deep Neural Networks ICML 2020 Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting JMLR 2020 Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization AISTATS 2020 CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles EMNLP 2020 CoRefi: A Crowd Sourcing Suite for Coreference Annotation EMNLP 2020 Cost-Accuracy Aware Adaptive Labeling for Active Learning AAAI 2020 Cost-Effective Interactive Attention Learning with Neural Attention Processes ICML 2020 Crowd-Assisted Disaster Scene Assessment with Human-AI Interactive Attention AAAI 2020 DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool COLING 2020 Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision CVPR 2020 Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient AACL 2020