LibFFM [Juan et al., 2016]
RocsDB by @sile
• BoTorch/Ax (Facebook)
HHVM JIT Compiler
• [Eric et al., 2010] Eric Brochu, Vlad M. Cora, and Nando de Freitas. A tutorial on Bayesian optimization of expensive cost
functions, with application to active user modeling and hierarchical reinforcement learning. 2010. arXiv:1012.2599.
• [James et al., 2011] James S. Bergstra, Remi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-
parameter optimization. In Advances in Neural Information Processing Systems 25. 2011.
• [Akiba at el., 2019] Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama. 2019. Optuna: A
Next-generation Hyperparameter Optimization Framework. In The 25th ACM SIGKDD Conference on Knowledge
Discovery and Data Mining (KDD ’19), August 4–8, 2019.
• [Jamieson and Talwalkar, 2016] K. Jamieson and A. Talwalkar. Non-stochastic best arm identiﬁcation and
hyperparameter optimization. In AISTATS, 2016.
• [Li et al., 2018] Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and
Ameet Talwalkar. Massively parallel hyperparameter tuning. arXiv preprint arXiv:1810.05934, 2018.
• [Eggensperger et al., 2013] Eggensperger, K., Feurer, M., Hutter, F., Bergstra, J., Snoek, J., Hoos, H., and Leyton-Brown,
K.: Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters, in NeurIPS workshop on
Bayesian Optimization in Theory and Practice (2013).
• [Juan et al., 2016] Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. Field-aware factorization machines
for CTR prediction. In RecSys, pages 43–50, 2016.