AutoML 2025 : AutoML Conference 2025
We welcome submissions on any topic touching upon automating any aspect of machine learning, broadly interpreted. If there is any question of fit, please feel free to contact the program chairs.
This year’s conference will have two parallel tracks: one on AutoML methods and one on applications, benchmarks, challenges, and datasets (ABCD) for AutoML. Papers accepted to either track will comprise the conference program on equal footing.
The following non-exhaustive lists provide examples of work in scope for each of these tracks:
**Methods Track**
- model selection (e.g., neural architecture search, ensembling)
- configuration/tuning (e.g., via evolutionary algorithms, Bayesian optimization)
- AutoML methodologies (e.g., reinforcement learning, meta-learning, in-context learning, warmstarting, portfolios, multi-objective optimization, constrained optimization)
- pipeline automation (e.g., automated data wrangling, feature engineering, pipeline synthesis, and configuration)
- automated procedures for diverse data (e.g., tabular, relational, multimodal, etc.)
- ensuring quality of results in AutoML (e.g., fairness, interpretability, trustworthiness, sustainability, robustness, reproducibility)
- supporting analysis and insight from automated systems
- context/prompt optimization
- dataset distillation / data selection / foundation datasets
- AutoML for multi-objective optimization
- Large language models
- etc.
**ABCD Track**
→ see also https://2024.automl.cc/?page_id=625 for more details
- Applications: open-source AutoML software and applications in this category that help us bridge the gap between theory and practice
- Benchmarks: submissions to further enhance the quality of benchmarking in AutoML
- Challenges: design, visions, analyses, methods and best practices for future and past challenges
- Datasets: new datasets, collections of datasets, or meta-datasets that open up new avenues of AutoML research
This year’s conference will have two parallel tracks: one on AutoML methods and one on applications, benchmarks, challenges, and datasets (ABCD) for AutoML. Papers accepted to either track will comprise the conference program on equal footing.
The following non-exhaustive lists provide examples of work in scope for each of these tracks:
**Methods Track**
- model selection (e.g., neural architecture search, ensembling)
- configuration/tuning (e.g., via evolutionary algorithms, Bayesian optimization)
- AutoML methodologies (e.g., reinforcement learning, meta-learning, in-context learning, warmstarting, portfolios, multi-objective optimization, constrained optimization)
- pipeline automation (e.g., automated data wrangling, feature engineering, pipeline synthesis, and configuration)
- automated procedures for diverse data (e.g., tabular, relational, multimodal, etc.)
- ensuring quality of results in AutoML (e.g., fairness, interpretability, trustworthiness, sustainability, robustness, reproducibility)
- supporting analysis and insight from automated systems
- context/prompt optimization
- dataset distillation / data selection / foundation datasets
- AutoML for multi-objective optimization
- Large language models
- etc.
**ABCD Track**
→ see also https://2024.automl.cc/?page_id=625 for more details
- Applications: open-source AutoML software and applications in this category that help us bridge the gap between theory and practice
- Benchmarks: submissions to further enhance the quality of benchmarking in AutoML
- Challenges: design, visions, analyses, methods and best practices for future and past challenges
- Datasets: new datasets, collections of datasets, or meta-datasets that open up new avenues of AutoML research