Summary of Bayesian Optimization For Unknown Cost-varying Variable Subsets with No-regret Costs, by Vu Viet Hoang et al.
Bayesian Optimization for Unknown Cost-Varying Variable Subsets with No-Regret Costs
by Vu Viet Hoang, Quoc Anh Hoang Nguyen, Hung Tran The
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Bayesian Optimization is a widely-used method for optimizing expensive-to-evaluate functions, but traditional BO assumes full control over all variables without additional constraints. This paper proposes a novel algorithm for Bayesian Optimization with cost-varying variable subsets (BOCVS), which balances informative subset selection against random sampling to minimize costs. The proposed algorithm separates the process into exploration and exploitation phases, filtering out low-quality subsets while leveraging high-quality ones. It achieves a sub-linear rate in both quality regret and cost regret, making it more effective than previous analyses. The algorithm outperforms comparable baselines across a wide range of benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian Optimization helps find the best solution by trying different options. But what if some options are too expensive? This paper creates a new way to balance finding the right solution with not spending too much money. It breaks down the process into two parts: exploring and exploiting. The first part finds good options, while the second part uses those options to find the best one. This new approach works better than previous methods and can be used in many different situations. |
Keywords
» Artificial intelligence » Optimization
