利用未来数据预测提升在线回归性能，实现更优学习效果。
Transductive and Learning-Augmented Online Regression
- 基于未来数据预测构建新型在线学习框架
- 预测越准，性能越接近理想情况，显著优于最坏情况
- 适用于可预测数据流场景，适合有先验信息的实用系统
针对数据流中存在可预测性的现实场景，研究在已知未来样本预测前提下的在线回归问题。在极端情形——转导式在线学习中，学习者在游戏开始前即获知全部样本序列，我们首次以胖破碎维数刻画其最小最大期望损失，明确区分了转导式与对抗性在线回归的性能边界。进一步推广至存在噪声或不完美预测的情形，基于转导结果设计了一种在线学习算法：其最小最大期望损失随预测质量平滑改善，在预测准确时显著优于最坏情况，接近转导学习表现。该方法使原本不可学习的类别在可预测条件下具备可学性，符合学习增强模型的整体范式。
原文摘要 · Abstract (English)
Motivated by the predictable nature of real-life in data streams, we study online regression when the learner has access to predictions about future examples. In the extreme case, called transductive online learning, the sequence of examples is revealed to the learner before the game begins. For this setting, we fully characterize the minimax expected regret in terms of the fat-shattering dimension, establishing a separation between transductive online regression and (adversarial) online regression. Then, we generalize this setting by allowing for noisy or \emph{imperfect} predictions about future examples. Using our results for the transductive online setting, we develop an online learner whose minimax expected regret matches the worst-case regret, improves smoothly with prediction quality, and significantly outperforms the worst-case regret when future example predictions are precise, achieving performance similar to the transductive online learner. This enables learnability for previously unlearnable classes under predictable examples, aligning with the broader learning-augmented model paradigm.
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