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.
- A review of automatic selection methods for machine learning algorithms and hyper-parameter values.
- PredicT-ML: a tool for automating machine learning model building with big clinical data.
- Aslib: A benchmark library for algorithm selection.
- FLASH: fast Bayesian optimization for data analytic pipelines
- Sequential model-based optimization for general algorithm configuration.
- Active network alignment: a matching-based approach, arXiv preprint arXiv:1610.05516 (2016).
- ベイジアン最適化について
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"
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。
