Literature - Githubissues

PhilippPro commented 7 years ago

I did a literature search (also for my other paper). Here are the papers divided by topic, maybe you can add yours to the specific topic, if you know some more.

Tuning in general:

Tuning with Iterated F-Racing: Automatic model selection for high-dimensional survival analysis
(iterated) F-Racing: F-Race and iterated F-Race: An overview, Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, and Thomas Stutzle
AutoML: Efficient and Robust Automated Machine Learning, Feurer et. al
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions, Bischl et al
mlrMBO: Faster Model-Based Optimization through Resource-Aware Scheduling Strategies, Richter et al
Hyperopt: a Python library for model selection and hyperparameter optimization (Pythons analogon to MBO)
Sequential MBO: Sequential Model-Based Optimization for General Algorithm Configuration, Hutter et al
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms, Thornton et al
Practical Bayesian Optimization of Machine Learning Algorithms

Meta-Learning:
Multi-Task Bayesian Optimization, Kwersky et. al
To tune or not to tune: recommending when to adjust SVM hyper-parameters via metalearning
Collaborative hyperparameter tuning, Bardenet et. al
Hyperparameter Optimization Machines, Wistuba et. al
Using meta-learning to initialize bayesian optimization of hyperparameters, Feurer et al
Scalable Hyperparameter Optimization with Products of Gaussian Process Experts, Schilling et. al
Sequential Model-Free Hyperparameter Tuning, Wistuba et. al
Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization, Wistuba et. al
Learning hyperparameter optimization initializations, Wistuba et. al

Hyperparameter Importance:
An Efficient Approach for Assessing Hyperparameter Importance
Identifying key algorithm parameters and instance features using forward selection
Analysing differences between algorithm configurations through ablation
Efficient Parameter Importance Analysis via Ablation with Surrogates
Hyper-parameter Tuning of a Decision Tree Induction Algorithm

Surrogate Models:
Surrogate benchmarks for hyperparameter optimization, Eggensperger et. al (RF is best)
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates

Time Prediction:
Prediction of Classifier Training Time Including Parameter Optimization, Reif et. al

Time AND Performance:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets, Klein et al

ja-thomas commented 7 years ago

You forgot the most important paper :(

https://arxiv.org/abs/1703.03373