The content introduces Information-based Transductive Active Learning (ITL) as a method to optimize active learning for targeted predictions. ITL adapts sampling to minimize uncertainty about specified prediction targets, demonstrating superior performance in fine-tuning neural networks and safe Bayesian optimization. The paper provides theoretical guarantees and practical applications of ITL in diverse scenarios.
The authors propose ITL as an approach to address the limitations of traditional active learning methods by focusing on specific prediction targets within constrained sample spaces. By maximizing information gain about these targets, ITL achieves superior performance compared to existing techniques. The paper presents theoretical results on the convergence of uncertainty reduction and applies ITL to real-world problems such as few-shot fine-tuning of neural networks and safe Bayesian optimization.
ITL is shown to converge uniformly to the smallest possible uncertainty obtainable from accessible data, offering a flexible framework applicable across various domains beyond those discussed in the paper. The method's effectiveness is demonstrated through experiments that highlight its superiority over conventional approaches in different scenarios.
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by Jona... om arxiv.org 03-13-2024
https://arxiv.org/pdf/2402.15898.pdfDiepere vragen