Batch Bayesian Active Learning with Partial Batch Label Sampling

arXiv:2510.09877 · cs.LG, cs.AI, stat.ML · Submitted 2025-10-10 · Read on arXiv

cs.LG, cs.AI, stat.ML

Submitted: 2025-10-10

Updated: 2026-10-12

Code: https://github.com/ADDAPT-ML/ParBaLS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Active Learning — 4× in this paper · explained in 6 episodes
• Acquisition Function — 1× in this paper · explained in 1 episode
• Embeddings — 1× in this paper · explained in 2 episodes
• Expected Predictive Information Gain — 1× in this paper
• Information Gain — 1× in this paper · explained in 2 episodes

Sources

• The iWildCam 2021 Competition Dataset
• Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
• Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers
• Active Learning with Expected Error Reduction

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: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks