Active Learning

Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally CVPR 2017 First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization NeurIPS 2017 Iterative Machine Teaching ICML 2017 Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback ACL 2017 Just Sort It! A Simple and Effective Approach to Active Preference Learning ICML 2017 Lean Crowdsourcing: Combining Humans and Machines in an Online System CVPR 2017 Learning Active Learning from Data NeurIPS 2017 Learning Algorithms for Active Learning ICML 2017 Learning a Neural Semantic Parser from User Feedback ACL 2017 Learning how to Active Learn: A Deep Reinforcement Learning Approach EMNLP 2017 Learning what to read: Focused machine reading EMNLP 2017 Learning with Feature Feedback: from Theory to Practice AISTATS 2017 Leveraging Union of Subspace Structure to Improve Constrained Clustering ICML 2017 Local algorithms for interactive clustering JMLR 2017 Lower Bounds on Active Learning for Graphical Model Selection AISTATS 2017 Max-value Entropy Search for Efficient Bayesian Optimization ICML 2017 Mining Object Parts From CNNs via Active Question-Answering CVPR 2017 Multi-instance multi-label active learning IJCAI 2017 Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection AISTATS 2017 Near-optimal Bayesian Active Learning with Correlated and Noisy Tests AISTATS 2017 Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs NeurIPS 2017 Noise-Tolerant Interactive Learning Using Pairwise Comparisons NeurIPS 2017 Non-Uniform Subset Selection for Active Learning in Structured Data CVPR 2017 On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models JMLR 2017 On Gleaning Knowledge from Multiple Domains for Active Learning IJCAI 2017