Eventos DaSCI

20 February, 2019

  • DaSCI Seminar - Frank Hutter - Deep Learning 2.0: Towards AI that Builds and Improves AI

    10/10/2023  4:00 pm - 5:00 pm

    Speaker: Frank Hutter 
    Title: Deep Learning 2.0: Towards AI that Builds and Improves AI
    Online Room: https://oficinavirtual.ugr.es/redes/SOR/SALVEUGR/accesosala.jsp?IDSALA=22975608
    Password: 914004
    Date: 10/10/2023
    Time: 16:00 (Spanish time)

    Abstract: Throughout the history of AI, there is a clear pattern that manual elements of AI methods are eventually replaced by better-performing automatically-found ones; for example, deep learning (DL) replaced manual feature engineering with learned representations. The logical next step in representation learning is to also (meta-)learn the best architectures for these representations, as well as the best algorithms & hyperparameters for learning them. In this talk, I will discuss various works with this goal in the area of AutoML, highlighting that AutoML can be efficient and arguing for an emphasis on multi-objective AutoML to also account for the various dimensions of trustworthiness (such as algorithmic fairness, robustness, and uncertainty calibration). Finally, taking the idea of meta-learning to the extreme, I will deep-dive into a novel approach that learns an entire classification algorithm for small tabular datasets that achieves a new state of the art at the cost of a single forward pass.

    Speaker: Frank Hutter is a Full Professor for Machine Learning at the University of Freiburg (Germany). He holds a PhD from the University of British Columbia (UBC, 2009), for which he received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada. He also won several best paper awards and prizes in international ML competitions. He is a Fellow of ELLIS and EurAI, Director of the ELLIS unit Freiburg, and the recipient of 3 ERC grants. Frank is best known for his research on automated machine learning (AutoML), including neural architecture search, efficient hyperparameter optimization, and meta-learning. 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, is co-teaching the first MOOC on AutoML, co-organized 15 AutoML-related workshops at ICML, NeurIPS and ICLR, and founded the AutoML conference as general chair in 2022.

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