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Invited talk, Frank Hutter
Frank Hutter
Author Information
Frank Hutter (University of Freiburg & Bosch)
Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he previously was an assistant professor 2013-2017. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.
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2023 Poster: Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars »
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2023 Poster: LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering »
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2023 Oral: Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition »
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2021 : CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning »
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2021 Workshop: 5th Workshop on Meta-Learning »
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2021 Poster: How Powerful are Performance Predictors in Neural Architecture Search? »
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2021 Poster: Well-tuned Simple Nets Excel on Tabular Datasets »
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2020 : Q/A for invited talk #1 »
Frank Hutter -
2020 : Meta-learning neural architectures, initial weights, hyperparameters, and algorithm components »
Frank Hutter -
2019 : Frank Hutter (University of Freiburg) "A Proposal for a New Competition Design Emphasizing Scientific Insights" »
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2019 Workshop: Meta-Learning »
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2016 : Frank Hutter (University Freiburg) »
Frank Hutter -
2016 Poster: Bayesian Optimization with Robust Bayesian Neural Networks »
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2016 Oral: Bayesian Optimization with Robust Bayesian Neural Networks »
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2015 : Scalable and Flexible Bayesian Optimization for Algorithm Configuration »
Frank Hutter -
2015 Poster: Efficient and Robust Automated Machine Learning »
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