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06/16/2022
Zero-Shot AutoML with Pretrained Models
Given a new dataset D and a low compute budget, how should we choose a p...
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10/15/2021
Improving Hyperparameter Optimization by Planning Ahead
Hyperparameter optimization (HPO) is generally treated as a bi-level opt...
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06/11/2021
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Hyperparameter optimization (HPO) is a core problem for the machine lear...
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02/07/2021
Hyperparameter Optimization with Differentiable Metafeatures
Metafeatures, or dataset characteristics, have been shown to improve the...
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06/27/2019
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
Hyperparameter tuning is an omnipresent problem in machine learning as i...
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06/24/2019
In Hindsight: A Smooth Reward for Steady Exploration
In classical Q-learning, the objective is to maximize the sum of discoun...
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05/27/2019