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[2602.14952] Locally Adaptive Multi-Objective Learning
Summary
The paper presents a novel approach to multi-objective learning that adapts to changing data distributions, enhancing prediction accuracy while addressing local guarantees.
Why It Matters
This research is significant as it tackles the challenges of multi-objective learning in dynamic environments, which is crucial for applications in fields like energy forecasting and algorithmic fairness. By improving adaptability, the findings could lead to more reliable AI systems that perform well under varying conditions.
Key Takeaways
- Introduces a locally adaptive method for multi-objective learning.
- Demonstrates improved performance on datasets related to energy forecasting and fairness.
- Addresses the limitations of existing methods in adapting to distribution shifts.
- Empirical evaluations show the proposed method achieves unbiased predictions.
- Highlights the importance of local guarantees in multi-objective frameworks.
Computer Science > Machine Learning arXiv:2602.14952 (cs) [Submitted on 16 Feb 2026] Title:Locally Adaptive Multi-Objective Learning Authors:Jivat Neet Kaur, Isaac Gibbs, Michael I. Jordan View a PDF of the paper titled Locally Adaptive Multi-Objective Learning, by Jivat Neet Kaur and 2 other authors View PDF HTML (experimental) Abstract: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....