Tinker: Hyperparameter Optimization Tool
Research Mentor(s)
Hutchinson, Brian
Description
Machine learning models can learn to recognize subtle patterns in complex data, making them useful in a wide variety of regression and classification tasks. The process of training a machine learning model almost always involves setting hyperparameters. Hyperparameters can be thought of as knobs that affect the learned model; some hyperparameters affect the architecture of the model itself, while others affect the optimization algorithm used the train it. To find (near) optimal hyperparameter configurations, researchers typically often hand-tune the models through tedious trial-and-error or simple heuristics. In this work, we are developing a web service that offers researchers easy access to powerful hyperparameter optimization routines, in order to eliminate the guesswork currently involved with hyperparameter tuning. Clients of our service will first provide possible ranges for the hyperparameters they wish to tune, creating a session. The service can then be queried to provide hyperparameter configurations for the client to try and, afterwards, report the results of this configuration. These results will then inform subsequent hyperparameter suggestions based on the chosen optimization module. Although some hyperparameter optimization services and tools currently exist, they are either prohibitively expensive or limited in functionality and cumbersome to use, making them inappropriate for many researchers and small companies. With our free service and easy-to-use interface, we aim to provide a valuable service to the long tail of machine learning model users.
Document Type
Event
Start Date
May 2018
End Date
May 2018
Department
Computer Science
Genre/Form
student projects, posters
Subjects – Topical (LCSH)
Machine learning; Reinforcement learning; Application software--Development; Artificial intelligence
Type
Image
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf
Tinker: Hyperparameter Optimization Tool
Machine learning models can learn to recognize subtle patterns in complex data, making them useful in a wide variety of regression and classification tasks. The process of training a machine learning model almost always involves setting hyperparameters. Hyperparameters can be thought of as knobs that affect the learned model; some hyperparameters affect the architecture of the model itself, while others affect the optimization algorithm used the train it. To find (near) optimal hyperparameter configurations, researchers typically often hand-tune the models through tedious trial-and-error or simple heuristics. In this work, we are developing a web service that offers researchers easy access to powerful hyperparameter optimization routines, in order to eliminate the guesswork currently involved with hyperparameter tuning. Clients of our service will first provide possible ranges for the hyperparameters they wish to tune, creating a session. The service can then be queried to provide hyperparameter configurations for the client to try and, afterwards, report the results of this configuration. These results will then inform subsequent hyperparameter suggestions based on the chosen optimization module. Although some hyperparameter optimization services and tools currently exist, they are either prohibitively expensive or limited in functionality and cumbersome to use, making them inappropriate for many researchers and small companies. With our free service and easy-to-use interface, we aim to provide a valuable service to the long tail of machine learning model users.