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UID:87727-1739887200-1739890800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Gittins Indices for Cost-aware and Freeze-thaw Bayesian Optimization
DESCRIPTION:Presented by Qian Xie\, Cornell \nHyperparameter optimization is crucial in real-world applications such as machine learning model training\, robotics control\, material design\, and plasma physics. In transportation\, hyperparameter optimization plays a significant role in applications like traffic flow prediction\, dynamic pricing\, route planning\, and public transportation scheduling\, where complex models need to be fine-tuned to achieve optimal performance. These scenarios are often modeled as black-box functions\, which take hyperparameters as inputs and output performance metrics. Bayesian optimization is a powerful framework for efficiently optimizing such black-box functions\, especially when evaluations are time-consuming or expensive. However\, practical factors such as varying function evaluation costs and observable partial feedback during function evaluation remain under-explored in this framework. My research leverages Gittins indices\, which are inherently cost-aware and feedback-aware\, by drawing connections to Pandora’s Box problems and Markovian/Bayesian bandits\, where Gittins indices are Bayesian optimal. \nIn the first half of my talk\, I will present my published work\, which adapts Gittins indices into a cost-aware acquisition function class for Bayesian optimization\, demonstrating competitive empirical performance\, particularly in medium-to-high dimensions. In the second half\, I will discuss my ongoing work on developing Gittins indices for freeze-thaw Bayesian optimization involving decisions on early stopping and switching of hyperparameter tests based on partial feedback.
URL:https://c2smart.engineering.nyu.edu/event/seminar-gittins-indices-for-cost-aware-and-freeze-thaw-bayesian-optimization/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Virtual Events,Webinars
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