Wei, P., Sagarna, R., Ke, Y., Ong, Y.-S. Easy-but-effective Domain Sub-similarity Learning for Transfer Regression. IEEE Transactions on Knowledge and Data Engineering, 1–1. doi:10.1109/tkde.2020.3039806
Abstract:
Transfer covariance functions, which can model domain similarities and adaptively control the knowledge transfer across domains, are widely used in transfer learning. In this paper, we concentrate on Gaussian process ( GP ) models using a transfer covariance function for regression problems in a black-box learning scenario. Precisely, we investigate a family of rather general transfer covariance functions, T∗ , that can model the heterogeneous sub-similarities of domains through multiple kernel learning. A necessary and sufficient condition to obtain valid GP s using T∗ ( GPT∗ ) for any data is given. This condition becomes specially handy for practical applications as (i) it enables semantic interpretations of the sub-similarities and (ii) it can readily be used for model learning. In particular, we propose a computationally inexpensive model learning rule that can explicitly capture different sub-similarities of domains. We propose two instantiations of GPT∗ , one with a set of predefined constant base kernels and one with a set of learnable parametric base kernels. Extensive experiments on 36 synthetic transfer tasks and 12 real-world transfer tasks demonstrate the effectiveness of GPT∗ on the sub-similarity capture and the transfer performance.
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Funding Info:
This research / project is supported by the National Research Foundation (NRF) - Singapore Data Science Consortium (SDSC) International Research Collaboration
Grant Reference no. : SDSC-2020-004