Papers
1–3 of 3Research Paper·Mar 8, 2026
Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typically rely on task-specific handcrafted metrics or static/s...
7.0 viability
Research Paper·Mar 9, 2026
Revisiting Unknowns: Towards Effective and Efficient Open-Set Active Learning
Open-set active learning (OSAL) aims to identify informative samples for annotation when unlabeled data may contain previously unseen classes-a common challenge in safety-critical and open-world scena...
7.0 viability
Research Paper·Mar 11, 2026
Adaptive Active Learning for Regression via Reinforcement Learning
Active learning for regression reduces labeling costs by selecting the most informative samples. Improved Greedy Sampling is a prominent method that balances feature-space diversity and output-space u...
7.0 viability