AdaO2B: Adaptive Online to Batch Conversion for Out-of-Distribution Generalization

Xiao Zhang, Sunhao Dai, Jun Xu, Yong Liu, Zhenhua Dong, Ji-Rong Wen

Keywords: online to batch conversion, out-of-distribution (OOD) generalization, streaming applications, bandit

TL;DR: An adaptive online to batch conversion approach that can handle out-of-distribution generalization.

Abstract: Online to batch conversion involves constructing a new batch learner by utilizing a series of models generated by an existing online learning algorithm, for achieving generalization guarantees under i.i.d assumption. However, when applied to real-world streaming applications such as streaming recommender systems, the data stream may be sampled from time-varying distributions instead of persistently being i.i.d. This poses a challenge in terms of out-of-distribution (OOD) generalization. Existing approaches employ fixed conversion mechanisms that are unable to adapt to novel testing distributions, hindering the testing accuracy of the batch learner. To address these issues, we propose AdaO2B, an adaptive online to batch conversion approach under the bandit setting. AdaO2B is designed to be aware of the distribution shifts in the testing data and achieves OOD generalization guarantees. Specifically, AdaO2B can dynamically combine the sequence of models learned by a contextual bandit algorithm and determine appropriate combination weights using a context-aware weighting function. This innovative approach allows for the conversion of a sequence of models into a batch learner that facilitates OOD generalization. Theoretical analysis provides justification for why and how the learned adaptive batch learner can achieve OOD generalization error guarantees. Experimental results have demonstrated that AdaO2B significantly outperforms state-of-the-art baselines on both synthetic data and real-world data.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 8830