Neural Architecture Search (NAS) is a very promisingbut still young field. I will start this talk by discussing various worksaiming to build a scientific community around NAS, including benchmarks, best practices, and open source frameworks. Then, I will discuss several exciting directions for the field: (1) a broad range of possible speedup techniques for NAS; (2) joint NAS + hyperparameter optimization in Auto-PyTorch to allow off-the-shelf AutoML; and (3) the extended problem definition of neural ensemble search (NES) that searches for a set of complementary architectures rather than a single one as in NAS.
Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief ExpertAutoML at the Bosch Center for Artificial Intelligence.
Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq.MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is the recipient of a 2013 Emmy Noether Fellowship, a 2016 ERC Starting Grant, a 2018 Google Faculty Research Award, a 2020 ERC PoC Award, and he is a Fellow of ELLIS.Frank?s recent research focuses on automated machine learning (AutoML), where he co-organized the ICML workshop series on AutoML every year since its inception in 2014, co-authored the prominent AutoML tools Auto-WEKA,Auto-sklearn, and Auto-PyTorch, won the first two AutoML challenges with his team, co-authored the first book on AutoML, worked extensively on efficient hyperparameter optimization and neural architecture search, andgave a NeurIPS 2018 tutorial with over 3000 attendees.