Efficient Contextual Preferential Bayesian Optimization with Historical Examples
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866912616075493376 |
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| author | Khan, Farha A. Chakraborty, Tanmay Dietrich, Jörg P. Wirth, Christian |
| author_facet | Khan, Farha A. Chakraborty, Tanmay Dietrich, Jörg P. Wirth, Christian |
| contents | State-of-the-art multi-objective optimization often assumes a known utility function, learns it interactively, or computes the full Pareto front-each requiring costly expert input.~Real-world problems, however, involve implicit preferences that are hard to formalize. To reduce expert involvement, we propose an offline, interpretable utility learning method that uses expert knowledge, historical examples, and coarse information about the utility space to reduce sample requirements. We model uncertainty via a full Bayesian posterior and propagate it throughout the optimization process. Our method outperforms standard Gaussian processes and BOPE across four domains, showing strong performance even with biased samples, as encountered in the real-world, and limited expert input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_10300 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Efficient Contextual Preferential Bayesian Optimization with Historical Examples Khan, Farha A. Chakraborty, Tanmay Dietrich, Jörg P. Wirth, Christian Machine Learning Artificial Intelligence 68T20 I.2.8; I.2.6 State-of-the-art multi-objective optimization often assumes a known utility function, learns it interactively, or computes the full Pareto front-each requiring costly expert input.~Real-world problems, however, involve implicit preferences that are hard to formalize. To reduce expert involvement, we propose an offline, interpretable utility learning method that uses expert knowledge, historical examples, and coarse information about the utility space to reduce sample requirements. We model uncertainty via a full Bayesian posterior and propagate it throughout the optimization process. Our method outperforms standard Gaussian processes and BOPE across four domains, showing strong performance even with biased samples, as encountered in the real-world, and limited expert input. |
| title | Efficient Contextual Preferential Bayesian Optimization with Historical Examples |
| topic | Machine Learning Artificial Intelligence 68T20 I.2.8; I.2.6 |
| url | https://arxiv.org/abs/2208.10300 |