Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)

Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria -- informativeness, representativeness, and diversity -- have been proposed for ALR. However, very few approaches in the literature have …

Pool-Based Sequential Active Learning for Regression

Active learning is a machine learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential active learning for regression (ALR). We first propose three essential criteria that an ALR approach should consider in selecting the most useful unlabeled samples: informativeness, representativeness, and diversity, and compare four existing ALR approaches against them. We then propose a new ALR approach usin…

Unsupervised Pool-Based Active Learning for Linear Regression

In many real-world machine learning applications, unlabeled data can be easily obtained, but it is very time-consuming and/or expensive to label them. So, it is desirable to be able to select the optimal samples to label, so that a good machine learning model can be trained from a minimum amount of labeled data. Active learning (AL) has been widely used for this purpose. However, most existing AL approaches are supervised: they train an initial model from a small amount of labeled samples, query new samples based on the model, and then update the model iteratively. Few of them have considered …

清池：深度主动学习中未标记池的渐进过滤

Cleaning the Pool: Progressive Filtering of Unlabeled Pools in Deep Active Learning

本文提出REFINE方法，通过结合多种主动学习策略，提升模型在不同数据集和任务中的性能，实验表明其优于单一策略和现有集成方法。

位置：完美AI对齐的复杂性——形式化RLHF三重困境

Position: The Complexity of Perfect AI Alignment -- Formalizing the RLHF Trilemma

研究提出RLHF三重困境，指出在安全、公平和稳健之间存在根本性权衡，并通过复杂性分析证明实现全球代表性需要超多项式计算资源。

特征加权提升回归问题的基于池的顺序主动学习

Feature Weighting Improves Pool-Based Sequential Active Learning for Regression

本文提出基于池的顺序主动学习方法，通过特征加权优化样本选择，提升回归模型的准确性，实验表明该方法在单任务和多任务回归中均有效。