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Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
Dheeraj Baby · Saurabh Garg · Tzu-Ching Yen · Sivaraman Balakrishnan · Zachary Lipton · Yu-Xiang Wang

Wed Dec 13 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #1701
This paper focuses on supervised and unsupervised online label shift,where the class marginals $Q(y)$ variesbut the class-conditionals $Q(x|y)$ remain invariant. In the unsupervised setting, our goal is to adapt a learner, trained on some offline labeled data, to changing label distributions given unlabeled online data. In the supervised setting, we must both learn a classifier and adapt to the dynamically evolving class marginals given only labeled online data. We develop novel algorithms that reduce the adaptation problem to online regression and guarantee optimal dynamic regret without any prior knowledge of the extent of drift in the label distribution. Our solution is based on bootstrapping the estimates of *online regression oracles* that track the drifting proportions. Experiments across numerous simulated and real-world online label shift scenarios demonstrate the superior performance of our proposed approaches, often achieving 1-3% improvement in accuracy while being sample and computationally efficient. Code is publicly available at https://github.com/Anon-djiwh/OnlineLabelShift

Author Information

Dheeraj Baby (UC Santa Barbara)
Saurabh Garg (Carnegie Mellon University)
Tzu-Ching Yen (Carnegie Mellon University)
Sivaraman Balakrishnan (Carnegie Mellon University)
Zachary Lipton (Carnegie Mellon University / Abridge)
Yu-Xiang Wang (UC Santa Barbara)

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