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Most Influential KDD 2019 Paper · 2026-03 edition

Optuna: A Next-generation Hyperparameter Optimization Framework

Takuya Akiba; Shotaro Sano; Toshihiko Yanase; Takeru Ohta; Masanori Koyama

Venue
ACM SIGKDD Conference (KDD) 2019
Recognition
Most Influential KDD 2019 Paper (Rank No. 1)
Edition
2026-03
Impact factor
8
Certificate ID
b97cde09b43279cf

Abstract

The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).

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