Locally Adaptive Multi-Objective Learning

TL;DR

This paper introduces a locally adaptive multi-objective learning method that improves predictive performance and fairness in non-stationary environments by incorporating an adaptive online algorithm, validated on energy and fairness datasets.

Contribution

It proposes a novel approach that enhances multi-objective learning with local adaptivity, addressing distribution shifts more effectively than prior methods.

Findings

Improved predictive accuracy over distribution shifts.

Achieved unbiased subgroup predictions.

Demonstrated robustness in real-world datasets.

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.