Skip to Main content Skip to Navigation
Conference papers

Hyperparameter optimization with approximate gradient

Abstract : Most models in machine learning contain at least one hyperparameter to control for model complexity. Choosing an appropriate set of hyperparameters is both crucial in terms of model accuracy and computationally challenging. In this work we propose an algorithm for the optimization of continuous hyperparameters using inexact gradient information. An advantage of this method is that hyperparameters can be updated before model parameters have fully converged. We also give sufficient conditions for the global convergence of this method, based on regularity conditions of the involved functions and summability of errors. Finally, we validate the empirical performance of this method on the estimation of regularization constants of L2-regularized logistic regression and kernel Ridge regression. Empirical benchmarks indicate that our approach is highly competitive with respect to state of the art methods.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-01386410
Contributor : Fabian Pedregosa <>
Submitted on : Monday, October 24, 2016 - 10:22:33 AM
Last modification on : Wednesday, September 23, 2020 - 4:29:45 AM

Links full text

Identifiers

  • HAL Id : hal-01386410, version 1
  • ARXIV : 1602.02355

Collections

Citation

Fabian Pedregosa. Hyperparameter optimization with approximate gradient. Proceedings of the 33rd International Conference on Machine Learning, Jun 2016, New York, United States. ⟨hal-01386410⟩

Share

Metrics

Record views

137