Thermal Budget Annealing performs feasible-first exploration in crash-prone hierarchical ML deployment spaces before exploitation with TPE, reducing wasted budget on invalid trials.
Bayesian Optimization
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Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
citing papers explorer
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Feasible-First Exploration for Constrained ML Deployment Optimization in Crash-Prone Hierarchical Search Spaces
Thermal Budget Annealing performs feasible-first exploration in crash-prone hierarchical ML deployment spaces before exploitation with TPE, reducing wasted budget on invalid trials.
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A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.