AutoML conference 2025 in NYC, September 8-11
Carola Doerr
Dear all,
The AutoML Conference is looking forward to welcoming you in New York City.
https://2025.automl.cc/
:: Sept 8-11, 2025
:: Submissions due: March 24, 2025 (abstracts due March 19)
:: Verizon Executive Education Center at Cornell Tech, Roosevelt Island, New York City, USA
:: confirmed keynotes :
Manuela Veloso, JP Morgan Chase
Andrew Gordon Wilson, New York University
Kevin Leyton-Brown, British Columbia University
:: Full Call For Papers: https://2025.automl.cc/call-for-papers/
:: videos from AutoML 2024 available at https://www.youtube.com/@automl_conf/playlists
:: proceedings from AutoML 2024 available at https://proceedings.mlr.press/v256/
:: follow us on social networks:
https://www.linkedin.com/company/automl-conf/
https://x.com/automl_conf
https://bsky.app/profile/automl-conf.bsky.social
:: general chair:
Roman Garnett, Washington University in St. Louis
:: PC chairs:
Leman Akoglu, Carnegie Mellon University
Carola Doerr, CNRS and Sorbonne University, FR
Jan van Rijn, Leiden University, NL
The main conference has two tracks: one focusing on methods advancing
the state of the art, and one focusing on applications, benchmarks,
challenges, and datasets (ABCD) to drive the development of new methods.
A non-exclusive list of topics is below.
Opportunities to submit other content (short papers, position papers, journal papers, etc.) will be coming soon!
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
- ...
ABCD Track
- 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