- Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning [2023, ICML]
- Towards Controlled Data Augmentations for Active Learning [2023, ICML]
- N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations [2022, ICML]
- Active Multi-Task Representation Learning [2022, ICML]
- ActiveHedge: Hedge meets Active Learning [2022, ICML]
- Uniform versus uncertainty sampling: When being active is less efficient than staying passive [2022, ICML]
- Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets [2022, ICML]
- Active Nearest Neighbor Regression Through Delaunay Refinement [2022, ICML]
- Bayesian Generative Active Deep Learning [ICML, 2019]:
- Adversarial active learning for deep networks: a margin based approach [2018, ICML]
- Deep Bayesian Active Learning with Image Data [ICML, 2017]
- Active learning for cost-sensitive classification [2017, ICML]
- Learning Algorithms for Active Learning [2017, ICML]
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [ICML, 2015]
- Active Learning for Multi-Objective Optimization [2013, ICML]
- Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization [2013, ICML]
- Batch Active Learning via Coordinated Matching [2012, ICML]
- Active learning for multi-task adaptive filtering [2010, ICML]
- Hierarchical Sampling for Active Learning [2008, ICML]
- Batch mode active learning and its application to medical image classification [2006, ICML]
- Agnostic Active Learning [2006, ICML]
- Diverse ensembles for active learning [2004, ICML]
- Active learning using pre-clustering [2004, ICML]
- Combining active learning and semisupervised learning using Gaussian fields and harmonic functions [2003, ICML]
- Incorporating diversity in active learning with support vector machines [2003, ICML]
- Active + Semi-Supervised Learning = Robust Multi-View Learning [2002, ICML]
- Toward optimal active learning through sampling estimation of error reduction [2001, ICML]: Error Reduction
- Employing EM and Pool-Based Active Learning for Text Classification [1998. ICML]:
- Query learning strategies using boosting and bagging [1998, ICML]
- Heterogeneous uncertainty sampling for supervised learning [1994, ICML]