自动机器学习（AutoML）领域论文合集（持续更新中）！！！

* 2017 | AutoLearn — Automated Feature Generation and Selection | Ambika Kaul, et al. | ICDM | [`PDF`](https://ieeexplore.ieee.org/document/8215494/)
* 2017 | One button machine for automating feature engineering in relational databases | Hoang Thanh Lam, et al. | arXiv | [`PDF`](https://arxiv.org/pdf/1706.00327.pdf)
* 2016 | Automating Feature Engineering | Udayan Khurana, et al. | NIPS | [`PDF`](http://workshops.inf.ed.ac.uk/nips2016-ai4datasci/papers/NIPS2016-AI4DataSci_paper_13.pdf)
* 2016 | ExploreKit: Automatic Feature Generation and Selection | Gilad Katz, et al. | ICDM | [`PDF`](http://ieeexplore.ieee.org/document/7837936/)
* 2015 | Deep Feature Synthesis: Towards Automating Data Science Endeavors | James Max Kanter, Kalyan Veeramachaneni | DSAA | [`PDF`](http://www.jmaxkanter.com/static/papers/DSAA_DSM_2015.pdf)

* 2016 | Cognito: Automated Feature Engineering for Supervised Learning | Udayan Khurana, et al. | ICDMW | [`PDF`](http://ieeexplore.ieee.org/document/7836821/)

* 2017 | Learning Feature Engineering for Classification | Fatemeh Nargesian, et al. | IJCAI | [`PDF`](https://www.ijcai.org/proceedings/2017/0352.pdf)

* 2017 | Feature Engineering for Predictive Modeling using Reinforcement Learning | Udayan Khurana, et al. | arXiv | [`PDF`](https://arxiv.org/pdf/1709.07150.pdf)
* 2010 | Feature Selection as a One-Player Game | Romaric Gaudel, Michele Sebag | ICML | [`PDF`](https://hal.archives-ouvertes.fr/inria-00484049/document)

* 2019 | Evolutionary Neural AutoML for Deep Learning | Jason Liang, et al. | arXiv | [`PDF`](https://arxiv.org/abs/1902.06827)
* 2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR | [`PDF`](https://arxiv.org/abs/1703.01041)
* 2002 | Evolving Neural Networks through Augmenting Topologies | Kenneth O.Stanley, Risto Miikkulainen | Evolutionary Computation | [`PDF`](http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf)

* 2017 | Simple and Efficient Architecture Search for Convolutional Neural Networks | Thomoas Elsken, et al. | ICLR | [`PDF`](https://arxiv.org/pdf/1711.04528.pdf)

* 2016 | Learning to Optimize | Ke Li, Jitendra Malik | arXiv | [`PDF`](https://arxiv.org/pdf/1606.01885.pdf)

* 2018 | AMC: AutoML for Model Compression and Acceleration on Mobile Devices | Yihui He, et al. | ECCV | [`PDF`](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yihui_He_AMC_Automated_Model_ECCV_2018_paper.pdf)
* 2018 | Efficient Neural Architecture Search via Parameter Sharing | Hieu Pham, et al. | arXiv | [`PDF`](https://arxiv.org/abs/1802.03268)
* 2017 | Neural Architecture Search with Reinforcement Learning | Barret Zoph, Quoc V. Le | ICLR | [`PDF`](https://arxiv.org/pdf/1611.01578.pdf)

* 2017 | Learning Transferable Architectures for Scalable Image Recognition | Barret Zoph, et al. | arXiv | [`PDF`](https://arxiv.org/abs/1707.07012)

* 2018 | Efficient Neural Architecture Search with Network Morphism | Haifeng Jin, et al. | arXiv | [`PDF`](https://arxiv.org/abs/1806.10282)

* 2018 | Neural Architecture Optimization | Renqian Luo, et al. | arXiv | [`PDF`](https://arxiv.org/abs/1808.07233)
* 2019 | DARTS: Differentiable Architecture Search | Hanxiao Liu, et al. | ICLR | [`PDF`](https://arxiv.org/abs/1806.09055)

* 2018 | A Tutorial on Bayesian Optimization. | [`PDF`](https://arxiv.org/pdf/1807.02811.pdf)
* 2018 | Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features | Mojmír Mutný, et al. | NeurIPS | [`PDF`](https://papers.nips.cc/paper/8115-efficient-high-dimensional-bayesian-optimization-with-additivity-and-quadrature-fourier-features.pdf)
* 2018 | High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. | PMLR | [`PDF`](https://arxiv.org/pdf/1802.07028v2.pdf)
* 2016 | Bayesian Optimization with Robust Bayesian Neural Networks | Jost Tobias Springenberg， et al. | NIPS | [`PDF`](https://papers.nips.cc/paper/6117-bayesian-optimization-with-robust-bayesian-neural-networks.pdf)
* 2016 | Scalable Hyperparameter Optimization with Products of Gaussian Process Experts | Nicolas Schilling, et al. | PKDD | [`PDF`](https://link.springer.com/chapter/10.1007/978-3-319-46128-1_3)
* 2016 | Taking the Human Out of the Loop: A Review of Bayesian Optimization | Bobak Shahriari, et al. | IEEE | [`PDF`](http://ieeexplore.ieee.org/document/7352306/)
* 2016 | Towards Automatically-Tuned Neural Networks | Hector Mendoza, et al. | JMLR | [`PDF`](http://aad.informatik.uni-freiburg.de/papers/16-AUTOML-AutoNet.pdf)
* 2016 | Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization | Martin Wistuba, et al. | PKDD | [`PDF`](https://link.springer.com/chapter/10.1007/978-3-319-46128-1_13)
* 2015 | Efficient and Robust Automated Machine Learning | [`PDF`](https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf)
* 2015 | Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | PKDD | [`PDF`](https://link.springer.com/chapter/10.1007/978-3-319-23525-7_6)
* 2015 | Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization | Martin Wistua, et al. | [`PDF`](https://dl.acm.org/citation.cfm?id=2991491)
* 2015 | Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | ICTAI | [`PDF`](http://ieeexplore.ieee.org/abstract/document/7372120/)
* 2015 | Learning Hyperparameter Optimization Initializations | Martin Wistuba, et al. | DSAA | [`PDF`](http://ieeexplore.ieee.org/abstract/document/7344817/)
* 2015 | Scalable Bayesian optimization using deep neural networks | Jasper Snoek, et al. | ACM | [`PDF`](https://dl.acm.org/citation.cfm?id=3045349)
* 2015 | Sequential Model-free Hyperparameter Tuning | Martin Wistuba, et al. | ICDM | [`PDF`](http://ieeexplore.ieee.org/abstract/document/7373431/)
* 2013 | Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms | [`PDF`](http://www.cs.ubc.ca/labs/beta/Projects/autoweka/papers/autoweka.pdf)
* 2013 | Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures | J. Bergstra | JMLR | [`PDF`](http://proceedings.mlr.press/v28/bergstra13.pdf)
* 2012 | Practical Bayesian Optimization of Machine Learning Algorithms | [`PDF`](https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf)
* 2011 | Sequential Model-Based Optimization for General Algorithm Configuration(extended version) | [`PDF`](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf)

* 2018 | Autostacker: A Compositional Evolutionary Learning System | Boyuan Chen, et al. | arXiv | [`PDF`](https://arxiv.org/pdf/1803.00684.pdf)
* 2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR | [`PDF`](https://arxiv.org/pdf/1703.01041.pdf)
* 2016 | Automating biomedical data science through tree-based pipeline optimization | Randal S. Olson, et al. | ECAL | [`PDF`](https://arxiv.org/pdf/1601.07925.pdf)
* 2016 | Evaluation of a tree-based pipeline optimization tool for automating data science | Randal S. Olson, et al. | GECCO | [`PDF`](https://dl.acm.org/citation.cfm?id=2908918)

* 2017 | Global Optimization of Lipschitz functions | C´edric Malherbe, Nicolas Vayatis | arXiv | [`PDF`](https://arxiv.org/pdf/1703.02628.pdf)

* 2009 | ParamILS: An Automatic Algorithm Configuration Framework | Frank Hutter, et al. | JAIR | [`PDF`](https://arxiv.org/pdf/1401.3492.pdf)

* 2008 | Cross-Disciplinary Perspectives on Meta-Learning for Algorithm Selection | [`PDF`](https://dl.acm.org/citation.cfm?id=1456656)
* 2019 | SMARTML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms | [`PDF`](http://openproceedings.org/2019/conf/edbt/EDBT19_paper_235.pdf)

* 2017 | Particle Swarm Optimization for Hyper-parameter Selection in Deep Neural Networks | Pablo Ribalta Lorenzo, et al. | GECCO | [`PDF`](https://dl.acm.org/citation.cfm?id=3071208)
* 2008 | Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines | Shih-Wei Lin, et al. | Expert Systems with Applications | [`PDF`](http://www.sciencedirect.com/science/article/pii/S0957417407003752)

* 2016 | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | Lisha Li, et al. | arXiv | [`PDF`](https://arxiv.org/pdf/1603.06560.pdf)
* 2012 | Random Search for Hyper-Parameter Optimization | James Bergstra, Yoshua Bengio | JMLR | [`PDF`](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf)
* 2011 | Algorithms for Hyper-parameter Optimization | James Bergstra, et al. | NIPS | [`PDF`](https://dl.acm.org/citation.cfm?id=2986743)

* 2016 | Efficient Transfer Learning Method for Automatic Hyperparameter Tuning | Dani Yogatama, Gideon Mann | JMLR | [`PDF`](https://pdfs.semanticscholar.org/75f2/6734972ebaffc6b43d45abd3048ef75f15a5.pdf)
* 2016 | Flexible Transfer Learning Framework for Bayesian Optimisation | Tinu Theckel Joy, et al. | PAKDD | [`PDF`](https://link.springer.com/chapter/10.1007/978-3-319-31753-3_9)
* 2016 | Hyperparameter Optimization Machines | Martin Wistuba, et al. | DSAA | [`PDF`](http://ieeexplore.ieee.org/abstract/document/7796889/)
* 2013 | Collaborative Hyperparameter Tuning | R´emi Bardenet, et al. | ICML | [`PDF`](http://proceedings.mlr.press/v28/bardenet13.pdf)