Recent papers library. Parsed and updated from my own Mendeley library.
Updated: 10/23/2024
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Google Vizier: A Service for Black-Box Optimization
Authors: Daniel Golovin and Benjamin Solnik and Subhodeep Moitra and Greg Kochanski and John Karro and D....
Year: 2017
Abstract: Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand. Hence, black-box optimization has become increasingly important as systems have become more complex. In this paper we describe Google Vizier, a Google-internal service for performing black-box optimization that has become the de facto parameter tuning engine at Google. Google Vizier is used to optimize many of our machine learning models and other systems, and also provides core capabilities to Google’s Cloud Machine Learning HyperTune subsystem. We discuss our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning and automated early stopping that the service provides.
Critical Reading Questions
1. What is the main research question or hypothesis, and what are the key findings?
This helps you understand the core purpose of the study and its primary results.
2. How sound is the methodology, and do the conclusions logically follow from the data?
This assesses the reliability of the research and the validity of its conclusions.
3. What are the implications and limitations of the study, and how does it contribute to the broader field?
This helps you contextualize the research and understand its significance and potential applications.