We had the pleasure to present our work on benchmarking ensembles in Automated Machine Learning (AutoML) at the first international Conference on Automated Machine Learning (AutoML-Conf 2022). We showed how we can use OpenML to create efficient benchmarks for ensembles in AutoML.

The AutoML-Conf 2022 provided a great place for many fruitful discussions. We were able to meet many other AutoML researchers in person. Moreover, the venue in Baltimore at the Johns Hopkins University provided a friendly environment for the conference.

We published our work in the late-breaking workshop track. Our paper, “Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML“, is publicly available. Likewise, our code is open-source and can be found here: https://github.com/ISG-Siegen/assembled.

The First AutoML Conference took place in July 2022 in Baltimore, Maryland. The conference brought together leading researchers, academics, and practitioners in the field of automated machine learning (AutoML) to discuss the latest developments, challenges, and opportunities in this rapidly advancing field. Attendees had the opportunity to hear from keynote speakers, participate in panel discussions, and network with other experts in the field.

AutoML is a subfield of machine learning that focuses on developing algorithms and systems that can automatically design, train, and tune machine learning models. The goal of AutoML is to make machine learning more accessible and efficient for practitioners, especially those with limited expertise in the field.

The First AutoML Conference was not the first conference in the field of machine learning. There are other conferences like NeurIPS, ICML, and ICLR which are some of the most well-known machine learning conferences and attract thousands of attendees from around the world. These conferences generally focus on a broad range of topics within machine learning and artificial intelligence, while the AutoML conference focuses specifically on Automated Machine Learning.

The topic of automated machine learning has gained a lot of attention in recent years, as it has the potential to democratize access to machine learning and make it more widely available to practitioners in various industries. AutoML systems can automate many of the tedious and time-consuming tasks associated with building machine learning models, such as feature engineering, model selection, and hyperparameter tuning. This can free up data scientists and other practitioners to focus on more important tasks, such as understanding and interpreting the models that are generated.

Baltimore is a city located in the state of Maryland, in the Mid-Atlantic region of the United States. It is best known for its rich history, culture, and landmarks. Visitors can take a stroll through the Inner Harbor, visit the National Aquarium, or take a tour of Fort McHenry, the birthplace of the American national anthem. Baltimore is also home to many museums and art galleries, including the Baltimore Museum of Art and the American Visionary Art Museum.

Getting to Baltimore is easy, it is well-connected by air, rail, and road. The Baltimore-Washington International Airport (BWI) is located just 10 miles south of the city, and there are many flights to Baltimore from major cities around the country. Once you are in Baltimore, you can get around the city by car, bus, or light rail. Taxis and ride-sharing services are also available, and the city has a bike-sharing program called B-Cycle.