deasonyuan/awesome-active-learning-1

Awesome Active Learning Paper List

Awesome Active Learning

A list of resources related to Active learning in machine learning.

Title	Venue
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision	CVPR'20
State-Relabeling Adversarial Active Learning	CVPR'20
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds	ICLR'20
Adversarial Sampling for Active Learning	WACV'20
Discriminative Active Learning	arXiv'19
Semantic Redundancies in Image-Classification Datasets: The 10% You Don’t Need	arXiv'19
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	NIPS'19
Bayesian Batch Active Learning as Sparse Subset Approximation	NIPS'19
Learning Loss for Active Learning	CVPR'19
Bayesian Generative Active Deep Learning	ICML'19
Variational Adversarial Active Learning	ICCV'19
Active Learning for Convolutional Neural Networks: A Core-Set Approach	ICLR'18
The Power of Ensembles for Active Learning in Image Classification	CVPR'18
Adversarial Active Learning for Sequence Labeling and Generation	IJCAI'18
Active Decision Boundary Annotation with Deep Generative Models	ICCV'17
Deep Bayesian Active Learning with Image Data	ICML'17
Generative Adversarial Active Learning	arXiv'17
Active Image Segmentation Propagation	CVPR'16
Cost-Effective Active Learning for Deep Image Classification	TCSVT'16

Tutorials

• [Book] Active Learning. Burr Settles. (CMU, 2012)
• [Youtube] Active Learning from Theory to Practice. Steve Hanneke, Robert Nowak. (ICML, 2019)

Papers

Pool-Based Sampling

Singleton

• A Variance Maximization Criterion for Active Learning. Yazhou Yang, Marco Loog. (Pattern Recognition, 2018)

• The power of ensembles for active learning in image classification. William H. Beluch, Tim Genewein, Andreas Nurnberger, Jan M. Kohler. (CVPR, 2018)

• Learning Algorithms for Active Learning. Philip Bachman, Alessandro Sordoni, Adam Trischler. (ICML, 2017)

• Learning Active Learning from Data. Ksenia Konyushkova, Sznitman Raphael. (NIPS, 2017)

• Beyond Disagreement-based Agnostic Active Learning. Chicheng Zhang, Kamalika Chaudhuri. (NIPS, 2014)

• Active Learning using On-line Algorithms. Chris Mesterharm, Michael J. Pazzani. (KDD, 2011)

• Active Learning from Crowds. Yan Yan, R ́omer Rosales, Glenn Fung, Jennifer G. Dy. (ICML, 2011)

• Hierarchical Sampling for Active Learning. Sanjoy Dasgupta, Daniel Hsu (ICML, 2008)

Batch/Batch-like

• Variational Adversarial Active Learning. Samarth Sinha, Sayna Ebrahimi, Trevor Darrell. (ICCV, 2019)
• Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning. Weishi Shi, Qi Yu. (NeurIPS, 2019)
• Rapid Performance Gain through Active Model Reuse. Feng Shi, Yu-Feng Li. (IJCAI, 2019)
• Meta-Learning for Batch Mode Active Learning. Sachin Ravi, Hugo Larochelle. (ICLR-WS, 2018)
• Active Semi-Supervised Learning Using Sampling Theory for Graph Signals. Akshay Gadde, Aamir Anis, Antonio Ortega. (KDD, 2014)
• Active Learning for Multi-Objective Optimization. Marcela Zuluaga, Andreas Krause, Guillaume Sergent, Markus P{''u}schel (ICML, 2013)
• Querying Discriminative and Representative Samples forBatch Mode Active Learning. Zheng Wang, Jieping Ye. (KDD, 2013)
• Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization. Yuxin Chen, Andreas Krause. (ICML, 2013)
• Active Learning for Probabilistic Hypotheses Usingthe Maximum Gibbs Error Criterion, Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu. (NIPS, 2013)
• Batch Active Learning via Coordinated Matching. Javad Azimi, Alan Fern, Xiaoli Z. Fern, Glencora Borradaile, Brent Heeringa. (ICML, 2012)
• Ask me better questions: active learning queries based on rule induction. Parisa Rashidi, Diane J. Cook. (KDD, 2011)
• Active Instance Sampling via Matrix Partition. Yuhong Guo. (NIPS 2010)
• Discriminative Batch Mode Active Learning. Charles X. Ling, Jun Du. (NIPS, 2007)

Stream-Based Selective Sampling

• Active Learning from Peers. Keerthiram Murugesan, Jaime Carbonell. (NIPS, 2017)
• An Analysis of Active Learning Strategies for Sequence Labeling Tasks. Burr Settles, Mark Craven. (EMNLP, 2008)
• Improving Generalization with Active Learning, DAVID COHN, LES ATLAS, RICHARD LADNER. (Machine Learning, 1994)

Membership Query Synthesize

• Active Learning via Membership Query Synthesisfor Semi-supervised Sentence Classification. Raphael Schumann, Ines Rehbein. (CoNLL, 2019)
• Active Learning with Direct Query Construction, Yuhong Guo, Dale Schuurmans. (KDD, 2008)

Others