Announcing the Automated Machine Learning Conference 2022

Modern machine learning systems come with many design decisions (including hyperparameters, architectures of neural networks and the entire data processing pipeline), and the idea of automating these decisions gave rise to the research field of automated machine learning (AutoML). AutoML has been booming over the last decade, with hundreds of papers published each year now in the subfield of neural architecture search alone. At the same time, AutoML matured considerably, and by now several AutoML systems support thousands of users in their projects.

In 2014, our journey towards an AutoML community started with the first international workshop on AutoML at ICML. Over the years, we co-organized 8 successful AutoML workshops, as well as the workshop series on Bayesian optimization, meta-learning, and neural architecture search. On top of this, there are AutoML-related workshops at different recent conferences, including ICML/NeurIPS/ICLR, IJCAI/AAAI, CVPR/ICCV, KDD, etc. This shows the huge interest in AutoML, but also led to a fragmentation of the community. We would like to change this and bring this community together.

Towards this end, we are excited to announce the 1st International Conference on AutoML in 2022. It will be co-located with ICML in Baltimore from July 25th to July 27th, 2022. Like ICML, in hopes that the COVID pandemic is under control in the summer, we currently plan for an in-person conference that will bring the community together physically.

Why yet another conference?

Community building is one of the central pillars and motivations for us to organize this conference. Meeting parts of the community is also possible at other conferences, but AutoML has grown so much that it deserves a home for bringing together all the different subfields of AutoML to exchange views, experiences and requirements. AutoML-Conf will provide this home.

Compared to the many thousands of participants at NeurIPS, CVPR, ICML and ICLR, AutoML-Conf will likely still be small enough to allow knowing a substantial fraction of attendees personally, making it much easier to build up strong connections, which in turn facilitates cross-fertilization and collaborations, and allows to form a sense of community.

Besides providing a dedicated conference that is on-topic for researchers in AutoML (rather than a session here and there at the mainstream conferences), having an AutoML conference also comes with the benefit of reducing the noise in the review process. Specifically, compared to conferences like ICML, ICLR, CVPR or NeurIPS, at AutoML-Conf it is much more likely that you get reviewers who are familiar with the topic of your AutoML paper.

What makes AutoML-Conf special?

Since collaborations often organically arise from sharing code, we strongly embrace open source, more so than other ML conferences. Open source also helps tackle large parts of the reproducibility crisis of ML and AutoML. Therefore, making code available is mandatory for publications at AutoML-Conf. We do, however, recognize that sharing certain aspects of the code or certain data sets is not possible; please see the author guidelines for these cases.

Making good open source code available is hard work: going the extra mile to really allow others to reproduce and use your work effectively. However, this is the quality that participants of AutoML-Conf can rely on, and the quality that will boost this great community even further.

In addition, we will also try to rethink how a conference can contribute to building a community. Sitting in a lecture hall most of the day and passively listening to talks helps you to learn something new, but only helps little for connecting to people. In fact, with papers and recorded talks being online weeks before conferences take place, consuming new content becomes less and less important at the conference. We envision a conference that encourages networking, interactions and discussions among attendees. Similar to awesome places for collaborations such as Dagstuhl (Germany) or NII Shonan (Japan), we plan for a mix of talks, invited poster sessions and small discussion rounds.

We will make recorded talks available before the conference to allow attendees to come prepared and network in a targeted fashion. At the conference itself, most papers will be presented as posters to allow for in-depth discussions. Of course, the recorded talks will remain online after the conference.

To be fair, we strive to accept all top-quality papers submitted to AutoML-Conf. We define “top-quality” similarly as other top-tier ML conferences, such as NeurIPS, ICML and ICLR (with the aforementioned emphasis on open-source and reproducibility), but we have no acceptance rate goal that we need to reach; we will rather consider a paper’s potential impact on the community in our final acceptance decisions. We hope that this will contribute towards reducing the noise in the reviewing process, while still maintaining or increasing the quality standard of accepted papers.

Owing to climate change, we will not force authors of accepted papers to attend in person (independent of the COVID situation), although we of course recommend it. We have, however, decided not to hold a full hybrid conference since it is even more important for the very first AutoML conference to meet in person, enabling direct interactions without any technical barriers, in order to build strong personal relationships and a sense of community.

Keynote Speakers

As you can see on, we will have keynotes by six renowned leaders in the field.

  • Anima Anandkumar (Caltech & NVIDIA) was the brain behind the AutoML framework Amazon Sagemaker and has made various contributions on network architectures.
  • Jeff Clune (University of British Columbia & OpenAI) is well known for his two Nature papers and his vision on AI generating algorithms.
  • Chelsea Finn (Stanford University) is without doubt one of the leading experts in the field of meta-learning.
  • Timnit Gebru showed over and over again the risks of AI if not carefully designed or applied to the wrong questions, a central question the AutoML community must face with increased adoption.
  • Julie Josse (INRIA Ecole Polytechnique) is a world expert in learning with missing values, a problem that is key to making AutoML applicable in many real-world applications.
  • Alex Smola (Amazon Web Services) has unique experiences in AutoML systems from developing Auto-Gluon and providing a cloud-based AutoML service.

Join us in this journey!

We are super excited about AutoML-Conf and hope that you will join us, the many other organizers and senior area chairs in this journey! Please visit for more details, share widely, and submit by the deadline (February 24, 2022 for abstracts, March 3, 2022 for full papers). We’re looking forward to seeing you at the conference!