Predictive Optimization with Zero-Shot Domain Adaptation
This work addresses predictive optimization across multiple domains, which is incremental as it extends existing methods to handle domain interactions.
The paper tackles the problem of predictive optimization with zero-shot domain adaptation, where a new domain description is found given a prediction, and demonstrates its potential usefulness through numerical experiments.
Prediction in a new domain without any training sample, called zero-shot domain adaptation (ZSDA), is an important task in domain adaptation. While prediction in a new domain has gained much attention in recent years, in this paper, we investigate another potential of ZSDA. Specifically, instead of predicting responses in a new domain, we find a description of a new domain given a prediction. The task is regarded as predictive optimization, but existing predictive optimization methods have not been extended to handling multiple domains. We propose a simple framework for predictive optimization with ZSDA and analyze the condition in which the optimization problem becomes convex optimization. We also discuss how to handle the interaction of characteristics of a domain in predictive optimization. Through numerical experiments, we demonstrate the potential usefulness of our proposed framework.