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Greg Kochanski

2 accepted papers

2022

Towards Learning Universal Hyperparameter Optimizers with Transformers

NeurIPS 2022accept

Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution. However, existing methods are restricted to learning from experiments sharing the same set of hyperparame…

2020

Gradientless Descent: High-Dimensional Zeroth-Order Optimization

ICLR 2020spotlight

Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$. In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerica…

Cited by 81SourceScholar