We consider the general problem of learning a predictor that satisfies multiple objectives of interest simultaneously, a broad framework that captures a range of specific learning goals including calibration, regret, and multiaccuracy. We work in an online setting where the data distribution can change arbitrarily over time. Existing approaches to this problem aim to minimize the set of objectives over the entire time horizon in a worst-case sense, and in practice they do not necessarily adapt to distribution shifts. Earlier work has aimed to alleviate this problem by incorporating additional objectives that target local guarantees over contiguous subintervals. Empirical evaluation of these proposals is, however, scarce. In this article, we consider an alternative procedure that achieves local adaptivity by replacing one part of the multi-objective learning method with an adaptive online algorithm. Empirical evaluations on datasets from energy forecasting and algorithmic fairness show that our proposed method improves upon existing approaches and achieves unbiased predictions over subgroups, while remaining robust under distribution shift.

翻译：我们研究同时满足多个目标的学习器构建这一通用问题，该框架涵盖包括校准度、遗憾值与多重准确性在内的多种具体学习目标。我们在数据分布可能随时间任意变化的在线学习场景下展开研究。现有方法致力于在最坏情况下最小化整个时间范围内的目标集合，但在实践中未必能适应分布偏移。先前研究尝试通过引入针对连续子区间的局部保证目标来缓解该问题，然而相关方案的实证评估尚不充分。本文提出一种替代方案，通过将多目标学习方法中的部分组件替换为自适应在线算法来实现局部自适应性。在能源预测与算法公平性数据集上的实证评估表明，所提方法相较于现有方案具有显著改进，能够在保持子群预测无偏性的同时，有效应对分布偏移的挑战。