Summary of Cages: Cost-aware Gradient Entropy Search For Efficient Local Multi-fidelity Bayesian Optimization, by Wei-ting Tang and Joel A. Paulson
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization
by Wei-Ting Tang, Joel A. Paulson
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of applying Bayesian optimization (BO) to high-dimensional search spaces by proposing a novel algorithm for local BO of multi-fidelity black-box functions. The proposed method, Cost-Aware Gradient Entropy Search (CAGES), makes no assumptions about the relationship between different information sources and employs an innovative information-theoretic acquisition function that enables efficient exploration. CAGES demonstrates significant performance improvements compared to state-of-the-art methods on various synthetic and reinforcement learning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the best way to optimize things without having to try everything, which is important when we don’t know what’s good or bad. The problem is that it gets harder to do this as the number of options grows. To solve this, researchers created a new method called CAGES that can use multiple sources of information to figure out how to find the best solution. This means we might not need to try everything, which could save time and money. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
