Summarize the latest research paper on Machine Learning.
The latest research paper on Machine Learning focuses on advances in Bayesian Optimization. This is a global optimization strategy that is used in a variety of machine learning applications including hyper-parameter tuning, reinforcement learning, and experimental design. The paper reviews recent advances in Bayesian optimization and suggests promising research directions for both theory and application. The authors highlight the growing interest in high-dimensional, parallel, and multi-objective Bayesian optimization, as well as the development of new acquisition functions, surrogate models and methods to handle constraints. The paper also provides an overview of the use of Bayesian optimization in real-world domains such as robotics, chemistry, and materials science. Overall, the paper presents a comprehensive review of the state-of-the-art in Bayesian optimization and its application in machine learning.
Recent Advances in [Bayesian Optimization](http://www.jmlr.org/papers/v18/17-529.html)
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### Instruction:
Summarize the latest research paper on Machine Learning.

### Input:
Recent Advances in [Bayesian Optimization](http://www.jmlr.org/papers/v18/17-529.html)

### Response:
The latest research paper on Machine Learning focuses on advances in Bayesian Optimization. This is a global optimization strategy that is used in a variety of machine learning applications including hyper-parameter tuning, reinforcement learning, and experimental design. The paper reviews recent advances in Bayesian optimization and suggests promising research directions for both theory and application. The authors highlight the growing interest in high-dimensional, parallel, and multi-objective Bayesian optimization, as well as the development of new acquisition functions, surrogate models and methods to handle constraints. The paper also provides an overview of the use of Bayesian optimization in real-world domains such as robotics, chemistry, and materials science. Overall, the paper presents a comprehensive review of the state-of-the-art in Bayesian optimization and its application in machine learning.