Transductive Active Learning: Theory and Applications

2024-02-13

AI Summary

This work addresses active learning in realistic settings where sampling is constrained to an accessible region, while prediction targets may lie outside this domain. To tackle this challenge, we propose a transductive active learning framework tailored to real-world prediction objectives, leveraging adaptive uncertainty minimization for efficient out-of-domain inference. Theoretically, we establish, for the first time under general regularity assumptions, the uniform consistency of the decision rule—guaranteeing convergence to the minimal uncertainty achievable by accessible data—providing strong and broadly applicable statistical guarantees. Methodologically, our approach integrates transductive learning, Bayesian optimization, and large language model (LLM) fine-tuning. Empirical evaluations demonstrate substantial improvements in sample efficiency on LLM active fine-tuning and safety-critical Bayesian optimization tasks, achieving state-of-the-art performance.

Technology Category

• Machine Learning: Active Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We study a generalization of classical active learning to real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside this region. We analyze a family of decision rules that sample adaptively to minimize uncertainty about prediction targets. We are the first to show, under general regularity assumptions, that such decision rules converge uniformly to the smallest possible uncertainty obtainable from the accessible data. We demonstrate their strong sample efficiency in two key applications: active fine-tuning of large neural networks and safe Bayesian optimization, where they achieve state-of-the-art performance.

Problem

• Generalization of active learning
• Minimizing uncertainty in predictions
• Applications in neural networks and Bayesian optimization

Innovation

• Adaptive sampling minimizes prediction uncertainty
• Uniform convergence to minimal uncertainty proven
• State-of-the-art in neural network fine-tuning