Understanding Uncertainty Sampling via Equivalent Loss
cs.LG, stat.ML
Submitted: 2023-07-06
Updated: 2026-09-05
Comments: An updated version of the previous paper titled "Understanding Uncertainty Sampling." Corrected some gaps and typos
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
- Margin-based sampling in high dimensions: When being active is less efficient than staying passive
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks