Presentation Title

Hyperparameter Optimization as a Web Service

Presentation Type

Poster

Abstract

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.

Start Date

6-5-2017 12:15 PM

End Date

16-5-2017 2:00 PM

Location

Miller Hall

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May 6th, 12:15 PM May 16th, 2:00 PM

Hyperparameter Optimization as a Web Service

Miller Hall

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.