ELLIS-AISA talk by Frank Hutter
We are pleased to announce an upcoming ELLIS-AISA talk by Frank Hutter (University of Freiburg) which will take place on July 13th, in room UN32.101 of the Vaihingen campus of the University of Stuttgart.
Deep Learning 2.0: Extending the Power of Deep Learning to the Meta-Level
Deep Learning (DL) has been incredibly successful, due to its ability to automatically acquire useful representations from raw data by a joint optimization process of all layers. However, despite this joint optimization of the network weights, current DL practice still requires substantial efforts to manually optimize on the meta-level to define the neural architecture and training hyperparameters for the data at hand. The next logical step is to jointly optimize these components as well, based on meta-level learning and optimization. I predict that this will allow the next generation of DL systems to simply accept data and user objectives to optimize for (which can, e.g., include fairness, robustness, uncertainty calibration, interpretability, etc) and to thereby provide a clean interface between domain experts (who best know the data and the relevant objectives for the application at hand, but do not need to be machine learning experts) on the one hand and the next-generation DL system on the other hand. In this talk, I will discuss several advances towards this goal, focussing on (1) the joint optimization of several meta-choices in the DL pipeline and (2) the efficiency of this meta-optimization, and (3) taking into account user objectives other than performance.
Frank Hutter is a Full Professor for Machine Learning at the University of Freiburg (Germany), as well as Chief Expert AutoML 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 a Fellow of ELLIS and EurAI and the recipient of 3 ERC grants. Frank is best known for his research on automated machine learning (AutoML), including neural architecture search and efficient hyperparameter optimization. He co-authored the first book on AutoML and the prominent AutoML tools Auto-WEKA, Auto-sklearn and Auto-PyTorch, won the first two AutoML challenges with his team, co-organized the ICML workshop series on AutoML every year 2014-2021, and is the general chair of the inaugural conference on AutoML 2022.
This talk is in collaboration with the Artificial Intelligence Software Academy of the University of Stuttgart.
Date: July 13, 2022
Place: Universitätstraße 32.101 (University of Stuttgart - Vaihingen Campus)
Looking forward to seeing you all there! No registration necessary.