ゆるふわめも

東京か京都にいます。

予測モデルのパイプライン作成・ハイパーパラメータ・チューニングの自動化に関する資料集

2015年のNIPS以降のものをメモしています。

論文リスト

Efficient and robust automated machine learning.

papers.nips.cc Feurer, Matthias, et al. "Efficient and robust automated machine learning." Advances in Neural Information Processing Systems. 2015.

A review of automatic selection methods for machine learning algorithms and hyper-parameter values.

link.springer.com Luo, Gang. "A review of automatic selection methods for machine learning algorithms and hyper-parameter values." Network Modeling Analysis in Health Informatics and Bioinformatics 5.1 (2016): 1-16.

PredicT-ML: a tool for automating machine learning model building with big clinical data.

hissjournal.biomedcentral.com Luo, Gang. "PredicT-ML: a tool for automating machine learning model building with big clinical data." Health Information Science and Systems 4.1 (2016): 1.

Aslib: A benchmark library for algorithm selection.

ASlib: A benchmark library for algorithm selection

Bischl, Bernd, et al. "Aslib: A benchmark library for algorithm selection." Artificial Intelligence 237 (2016): 41-58.

FLASH: fast Bayesian optimization for data analytic pipelines

[1602.06468] FLASH: Fast Bayesian Optimization for Data Analytic Pipelines

Zhang, Yuyu, et al. "FLASH: fast Bayesian optimization for data analytic pipelines." arXiv preprint arXiv:1602.06468 (2016).

Sequential model-based optimization for general algorithm configuration.

Sequential Model-Based Optimization for General Algorithm Configuration - Springer

Sequential model-based optimization for general algorithm configuration

SMAC – Machine Learning for Automated Algorithm Design

Active network alignment: a matching-based approach, arXiv preprint arXiv:1610.05516 (2016).

[1610.05516] Active network alignment: a matching-based approach

Eric Malmi, Evimaria Terzi, Aristides Gionis, "Active network alignment: a matching-based approach"

github.com

ベイジアン最適化について

後で調べる予定。とりあえず以下の方が入門の資料をまとめてくれていました。

qiita.com