DeepAI AI Chat
Log In Sign Up

Hyperparameter Optimisation with Early Termination of Poor Performers

by   Dobromir Marinov, et al.

It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally; indeed, training a machine learning system on real data with just a single combination of hyperparameters usually takes hours or even days. In this paper, we address this challenge by trying to predict the performance of the machine learning system with a given combination of hyperparameters without completing the expensive learning process. Instead, we terminate the training process at an early stage, collect the model performance data and use it to predict which of the combinations of hyperparameters is most promising. Our preliminary experiments show that such a prediction improves the performance of the commonly used random search approach.


page 1

page 2

page 3


Hyperparameter Search in Machine Learning

We introduce the hyperparameter search problem in the field of machine l...

Weighted Random Search for CNN Hyperparameter Optimization

Nearly all model algorithms used in machine learning use two different s...

A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning

A Reinforcement Learning (RL) system depends on a set of initial conditi...

Mathematically Modeling the Lexicon Entropy of Emergent Language

We formulate a stochastic process, FiLex, as a mathematical model of lex...

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) is a promising framework for l...

Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters

Machine learning methods are being increasingly used in most technical a...

A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification

Node embedding methods find latent lower-dimensional representations whi...