Boosting has garnered significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval-censored data. This type of data is common in survival analysis and time-to-event studies where exact event times are unobserved but fall within known intervals. Effective handling of such data is crucial in fields like medical research, reliability engineering, and social sciences. In this work, we introduce novel nonparametric boosting methods for regression and classification tasks with interval-censored data. Our approaches leverage censoring unbiased transformations to adjust loss functions and impute transformed responses while maintaining model accuracy. Implemented via functional gradient descent, these methods ensure scalability and adaptability. We rigorously establish their theoretical properties, including optimality and mean squared error trade-offs. Our proposed methods not only offer a robust framework for enhancing predictive accuracy in domains where interval-censored data are common but also complement existing work, expanding the applicability of existing boosting techniques. Empirical studies demonstrate robust performance across various finite-sample scenarios, highlighting the practical utility of our approaches.

翻译：Boosting方法在机器学习与统计学领域均引起了广泛关注。传统Boosting算法专为完全观测的随机样本设计，在处理现实问题（尤其是区间删失数据）时常面临挑战。此类数据常见于生存分析与事件时间研究，其中确切事件时间未被观测但已知其所在区间。在医学研究、可靠性工程和社会科学等领域，对此类数据的有效处理至关重要。本研究针对区间删失数据的回归与分类任务，提出了新颖的非参数Boosting方法。我们的方法利用删失无偏变换调整损失函数并填补变换后的响应变量，同时保持模型精度。通过函数梯度下降实现，这些方法确保了可扩展性与适应性。我们严格论证了其理论性质，包括最优性与均方误差权衡。所提出的方法不仅为区间删失数据常见领域提供了提升预测精度的稳健框架，而且对现有研究形成补充，拓展了现有Boosting技术的适用性。实证研究在多种有限样本场景中均展现出稳健性能，凸显了本方法的实用价值。