Awesome-AutoML

Curating a list of AutoML-related research, tools, projects and other resources

AutoML

AutoML is the tools and technology to use machine learning methods and processes to automate machine learning systems and make them more accessible. It existed for several decades so it's not a completely new idea.

Recent work by Google Brain and many others have re-kindled the enthusiasm of AutoML and some companies have already commercialized the technology. Thus, it has becomes one of the hosttest areas to look into.

There are many kinds of AutoML, including:

• Neural network architecture search
• Hyperparameter optimization
• Optimizer search
• Data augmentation search
• Learning to learn/Meta-learning
• And many more

Research papers

AutoML survey

• Neural architecture search: a survey 深度神经网络结构搜索综述 (Tang et al. 2021)
• AutoML to Date and Beyond: Challenges and Opportunities (Santu et al. 2020)
• A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions (Ren et al. 2020)
• On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (Yang et al. 2020)
• Benchmark and Survey of Automated Machine Learning Frameworks (Zoller et al. 2019)
• AutoML: A Survey of the State-of-the-Art (He et al. 2019)
• A Survey on Neural Architecture Search (Wistuba et al. 2019)
• Neural Architecture Search: A Survey (Elsken et al. 2019)
• Taking Human out of Learning Applications: A Survey on Automated Machine Learning (Yao et al. 2018)

Neural Architecture Search

• utoResearch-RL: Perpetual Self-Evaluating Reinforcement Learning Agents for Autonomous Neural Architecture Discovery (Jain et al. 2026)
• AlphaGo Moment for Model Architecture Discovery (Ling et al. 2025)
• LayerNAS: Neural Architecture Search in Polynomial Complexity (Fan et al. 2023)
• EvoPrompting: Language Models for Code-Level Neural Architecture Search (Chen et al. 2023)
• Neural Architecture Search using Property Guided Synthesis (Jin et al. 2022)
• Data-Free Neural Architecture Search via Recursive Label Calibration (Liu et al. 2022)
• Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs (Akin et al. 2022)
• Resource-Constrained Neural Architecture Search on Tabular Datasets (Yang et al. 2022)
• Searching for Fast Model Families on Datacenter Accelerators (Li et al. 2022)
• Towards the co-design of neural networks and accelerators (Zhou et al. 2022)
• Neural Architecture Search for Energy Efficient Always-on Audio Models (Speckhard et al. 2022)
• KNAS: Green Neural Architecture Search (Xu et al. 2021)
• Primer: Searching for Efficient Transformers for Language Modeling (So et al. 2021)
• NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search (Xu et al. 2021)
• Accelerating Neural Architecture Search for Natural Language Processing with Knowledge Distillation and Earth Mover's Distance (Li et al. 2021)
• [AlphaNet: Improved Training of Supernets with