awesome-automl-papers

2019 | AutoML: A Survey of the State-of-the-Art | Xin He, et al. | arXiv |

2019 | Survey on Automated Machine Learning | Marc Zoeller, Marco F. Huber | arXiv |

2019 | Automated Machine Learning: State-of-The-Art and Open Challenges | Radwa Elshawi, et al. | arXiv |

2018 | Taking Human out of Learning Applications: A Survey on Automated Machine Learning | Quanming Yao, et al. | arXiv |

2020 | On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice | Li Yang, et al. | Neurocomputing |

2020 | Automated Machine Learning--a brief review at the end of the early years | Escalante, H. J. | arXiv |

2022 | IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective | Li Yang, et al. | arXiv |

2024 | Automated machine learning: past, present and future | Baratchi. M, et al. | Artificial Intelligence Review |

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2022 | BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML | Mehdi Bahrami, et al. | AutoML |

2017 | AutoLearn — Automated Feature Generation and Selection | Ambika Kaul, et al. | ICDM |

2017 | One button machine for automating feature engineering in relational databases | Hoang Thanh Lam, et al. | arXiv |

2016 | Automating Feature Engineering | Udayan Khurana, et al. | NIPS |

2016 | ExploreKit: Automatic Feature Generation and Selection | Gilad Katz, et al. | ICDM |

2015 | Deep Feature Synthesis: Towards Automating Data Science Endeavors | James Max Kanter, Kalyan Veeramachaneni | DSAA |

2016 | Cognito: Automated Feature Engineering for Supervised Learning | Udayan Khurana, et al. | ICDMW |

2020 | AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space | Chengrun Yang, et al. | KDD |

2017 | Learning Feature Engineering for Classification | Fatemeh Nargesian, et al. | IJCAI |

2017 | Feature Engineering for Predictive Modeling using Reinforcement Learning | Udayan Khurana, et al. | arXiv |

2010 | Feature Selection as a One-Player Game | Romaric Gaudel, Michele Sebag | ICML |

2019 | Evolutionary Neural AutoML for Deep Learning | Jason Liang, et al. | GECCO |

2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR |

2002 | Evolving Neural Networks through Augmenting Topologies | Kenneth O.Stanley, Risto Miikkulainen | Evolutionary Computation |

2017 | Simple and Efficient Architecture Search for Convolutional Neural Networks | Thomoas Elsken, et al. | ICLR |

2016 | Learning to Optimize | Ke Li, Jitendra Malik | arXiv |

2018 | AMC: AutoML for Model Compression and Acceleration on Mobile Devices | Yihui He, et al. | ECCV |

2018 | Efficient Neural Architecture Search via Parameter Sharing | Hieu Pham, et al. | arXiv |

2017 | Neural Architecture Search with Reinforcement Learning | Barret Zoph, Quoc V. Le | ICLR |

2017 | Learning Transferable Architectures for Scalable Image Recognition | Barret Zoph, et al. | arXiv |

2019 | Auto-Keras: An Efficient Neural Architecture Search System | Haifeng Jin, et al. | KDD |

2018 | Neural Architecture Optimization | Renqian Luo, et al. | arXiv |

2019 | DARTS: Differentiable Architecture Search | Hanxiao Liu, et al. | ICLR |

2021 | SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning | Pyeon, et al. | ICLR |

2019 | Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning | Jiayi Liu, et al. | IEEE Big Data |

2019 | Towards modular and programmable architecture search | Renato Negrinho, et al. | NeurIPS |

2019 | Evolutionary Neural AutoML for Deep Learning | Jason Liang, et al. | arXiv |

2017 | ATM: A Distributed, Collaborative, Scalable System for Automated Machine Learning | T. Swearingen, et al. | IEEE |

2017 | Google Vizier: A Service for Black-Box Optimization | Daniel Golovin, et al. | KDD |

2015 | AutoCompete: A Framework for Machine Learning Competitions | Abhishek Thakur, et al. | ICML |

2020 | Bayesian Optimization of Risk Measures | NeurIPS |

2020 | BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization | NeurIPS |

2020 | Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly | JMLR |

2019 | Bayesian Optimization with Unknown Search Space | NeurIPS |

2019 | Constrained Bayesian optimization with noisy experiments |

2019 | Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning | NeurIPS |

2019 | Practical Two-Step Lookahead Bayesian Optimization | NeurIPS |

2019 | Predictive entropy search for multi-objective bayesian optimization with constraints |

2018 | BOCK: Bayesian optimization with cylindrical kernels | ICML |

2018 | Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features | Mojmír Mutný, et al. | NeurIPS |

2018 | High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. | PMLR |

2018 | Maximizing acquisition functions for Bayesian optimization | NeurIPS |

2018 | Scalable hyperparameter transfer learning | NeurIPS |

2016 | Bayesian Optimization with Robust Bayesian Neural Networks | Jost Tobias Springenberg， et al. | NIPS |

2016 | Scalable Hyperparameter Optimization with Products of Gaussian Process Experts | Nicolas Schilling, et al. | PKDD |

2016 | Taking the Human Out of the Loop: A Review of Bayesian Optimization | Bobak Shahriari, et al. | IEEE |

2016 | Towards Automatically-Tuned Neural Networks | Hector Mendoza, et al. | JMLR |

2016 | Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization | Martin Wistuba, et al. | PKDD |

2015 | Efficient and Robust Automated Machine Learning |

2015 | Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | PKDD |

2015 | Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization | Martin Wistua, et al. |

2015 | Joint Model Choice and Hyperparameter Optimization with Factorized Multilayer Perceptrons | Nicolas Schilling, et al. | ICTAI |

2015 | Learning Hyperparameter Optimization Initializations | Martin Wistuba, et al. | DSAA |

2015 | Scalable Bayesian optimization using deep neural networks | Jasper Snoek, et al. | ACM |

2015 | Sequential Model-free Hyperparameter Tuning | Martin Wistuba, et al. | ICDM |

2013 | Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms |

2013 | Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures | J. Bergstra | JMLR |

2012 | Practical Bayesian Optimization of Machine Learning Algorithms |

2011 | Sequential Model-Based Optimization for General Algorithm Configuration(extended version) |

2020 | Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians | Juhan Bae, Roger Grosse | Neurips |

2018 | Autostacker: A Compositional Evolutionary Learning System | Boyuan Chen, et al. | arXiv |

2017 | Large-Scale Evolution of Image Classifiers | Esteban Real, et al. | PMLR |

2016 | Automating biomedical data science through tree-based pipeline optimization | Randal S. Olson, et al. | ECAL |

2016 | Evaluation of a tree-based pipeline optimization tool for automating data science | Randal S. Olson, et al. | GECCO |

2017 | Global Optimization of Lipschitz functions | C´edric Malherbe, Nicolas Vayatis | arXiv |

2009 | ParamILS: An Automatic Algorithm Configuration Framework | Frank Hutter, et al. | JAIR |

2019 | OBOE: Collaborative Filtering for AutoML Model Selection | Chengrun Yang, et al. | KDD |

2019 | SMARTML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms |

2008 | Cross-Disciplinary Perspectives on Meta-Learning for Algorithm Selection |

2017 | Particle Swarm Optimization for Hyper-parameter Selection in Deep Neural Networks | Pablo Ribalta Lorenzo, et al. | GECCO |

2008 | Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines | Shih-Wei Lin, et al. | Expert Systems with Applications |

2016 | Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | Lisha Li, et al. | arXiv |

2012 | Random Search for Hyper-Parameter Optimization | James Bergstra, Yoshua Bengio | JMLR |

2011 | Algorithms for Hyper-parameter Optimization | James Bergstra, et al. | NIPS |

2016 | Efficient Transfer Learning Method for Automatic Hyperparameter Tuning | Dani Yogatama, Gideon Mann | JMLR |

2016 | Flexible Transfer Learning Framework for Bayesian Optimisation | Tinu Theckel Joy, et al. | PAKDD |

2016 | Hyperparameter Optimization Machines | Martin Wistuba, et al. | DSAA |

2013 | Collaborative Hyperparameter Tuning | R´emi Bardenet, et al. | ICML |

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2020 | Automated Machine Learning Techniques for Data Streams | Alexandru-Ionut Imbrea |

2018 | Accelerating Neural Architecture Search using Performance Prediction | Bowen Baker, et al. | ICLR |

2017 | Automatic Frankensteining: Creating Complex Ensembles Autonomously | Martin Wistuba, et al. | SIAM |

2018 | Characterizing classification datasets: A study of meta-features for meta-learning | Rivolli, Adriano, et al. | arXiv |

2020 | Putting the Human Back in the AutoML Loop | Xanthopoulos, Iordanis, et al. | EDBT/ICDT |

2018 | A Tutorial on Bayesian Optimization. |

2010 | A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning |

2008 | Metalearning - A Tutorial |

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