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- affiliation: University of Freiburg, Germany
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2020 – today
- 2022
[j26]Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp
, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer:
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. J. Artif. Intell. Res. 74: 517-568 (2022)- [j25]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter:
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. J. Mach. Learn. Res. 23: 54:1-54:9 (2022) - [i104]Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Júlio C. S. Jacques Júnior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sébastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arber Zela, Yang Zhang:
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019. CoRR abs/2201.03801 (2022) - [i103]Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer:
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. CoRR abs/2201.03916 (2022) - [i102]Yash Mehta, Colin White, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, Frank Hutter:
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy. CoRR abs/2201.13396 (2022) - [i101]Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter:
Learning Synthetic Environments and Reward Networks for Reinforcement Learning. CoRR abs/2202.02790 (2022) - [i100]André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr:
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration. CoRR abs/2202.03259 (2022) - [i99]Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
Contextualize Me - The Case for Context in Reinforcement Learning. CoRR abs/2202.04500 (2022) - [i98]Thomas Elsken, Arber Zela, Jan Hendrik Metzen, Benedikt Staffler, Thomas Brox, Abhinav Valada, Frank Hutter:
Neural Architecture Search for Dense Prediction Tasks in Computer Vision. CoRR abs/2202.07242 (2022) - [i97]Niklas Hasebrook, Felix Morsbach, Niclas Kannengießer, Jörg K. H. Franke, Frank Hutter, Ali Sunyaev:
Why Do Machine Learning Practitioners Still Use Manual Tuning? A Qualitative Study. CoRR abs/2203.01717 (2022) - [i96]Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi:
πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. CoRR abs/2204.11051 (2022) - [i95]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. CoRR abs/2205.05511 (2022) - [i94]Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter:
Automated Dynamic Algorithm Configuration. CoRR abs/2205.13881 (2022) - [i93]Jörg K. H. Franke, Frederic Runge, Frank Hutter:
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design. CoRR abs/2205.13927 (2022) - [i92]René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer:
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. CoRR abs/2206.03493 (2022) - [i91]Carl Hvarfner, Frank Hutter, Luigi Nardi:
Joint Entropy Search For Maximally-Informed Bayesian Optimization. CoRR abs/2206.04771 (2022) - [i90]Adrian El Baz, André Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Shell Hu, Frank Hutter, Zhengying Liu, Felix Mohr, Jan N. van Rijn, Xin Wang, Isabelle Guyon:
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. CoRR abs/2206.08138 (2022) - [i89]Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter:
Zero-Shot AutoML with Pretrained Models. CoRR abs/2206.08476 (2022) - 2021
- [j24]Mauro Vallati, Lukás Chrpa, Thomas Leo McCluskey, Frank Hutter:
On the Importance of Domain Model Configuration for Automated Planning Engines. J. Autom. Reason. 65(6): 727-773 (2021) - [j23]Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter:
OpenML-Python: an extensible Python API for OpenML. J. Mach. Learn. Res. 22: 100:1-100:5 (2021) - [j22]Lucas Zimmer, Marius Lindauer, Frank Hutter:
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3079-3090 (2021) - [j21]Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Júlio C. S. Jacques Júnior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sébastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arber Zela, Yang Zhang:
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3108-3125 (2021) - [c87]David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer:
Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS 2021: 597-605 - [c86]Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra:
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. AISTATS 2021: 4015-4023 - [c85]Samuel G. Müller, Frank Hutter:
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. ICCV 2021: 754-762 - [c84]Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter:
Sample-Efficient Automated Deep Reinforcement Learning. ICLR 2021 - [c83]André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer:
TempoRL: Learning When to Act. ICML 2021: 914-924 - [c82]Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer:
Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML 2021: 2948-2958 - [c81]Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer:
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI 2021: 1668-1674 - [c80]Noor H. Awad, Neeratyoy Mallik, Frank Hutter:
DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization. IJCAI 2021: 2147-2153 - [c79]Jovita Lukasik, David Friede, Arber Zela, Frank Hutter, Margret Keuper:
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. IJCNN 2021: 1-8 - [c78]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren:
OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks 2021 - [c77]Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter:
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. NeurIPS Datasets and Benchmarks 2021 - [c76]Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh:
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. NeurIPS 2021: 7898-7911 - [c75]Shen Yan, Colin White, Yash Savani, Frank Hutter:
NAS-Bench-x11 and the Power of Learning Curves. NeurIPS 2021: 22534-22549 - [c74]Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka:
Well-tuned Simple Nets Excel on Tabular Datasets. NeurIPS 2021: 23928-23941 - [c73]Colin White, Arber Zela, Robin Ru, Yang Liu, Frank Hutter:
How Powerful are Performance Predictors in Neural Architecture Search? NeurIPS 2021: 28454-28469 - [c72]Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter:
Bayesian Optimization with a Prior for the Optimum. ECML/PKDD (3) 2021: 265-296 - [p8]Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Automated Configuration and Selection of SAT Solvers. Handbook of Satisfiability 2021: 481-507 - [e7]Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part I. Lecture Notes in Computer Science 12457, Springer 2021, ISBN 978-3-030-67657-5 [contents] - [e6]Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12458, Springer 2021, ISBN 978-3-030-67660-5 [contents] - [e5]Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part III. Lecture Notes in Computer Science 12459, Springer 2021, ISBN 978-3-030-67663-6 [contents] - [i88]Fabio Ferreira, Thomas Nierhoff, Frank Hutter:
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies. CoRR abs/2101.09721 (2021) - [i87]Samuel Müller, André Biedenkapp, Frank Hutter:
In-Loop Meta-Learning with Gradient-Alignment Reward. CoRR abs/2102.03275 (2021) - [i86]Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra:
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. CoRR abs/2102.13651 (2021) - [i85]Samuel G. Müller, Frank Hutter:
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. CoRR abs/2103.10158 (2021) - [i84]Colin White, Arber Zela, Binxin Ru, Yang Liu, Frank Hutter:
How Powerful are Performance Predictors in Neural Architecture Search? CoRR abs/2104.01177 (2021) - [i83]Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Guilherme Miotto, Simon Schrodi, Andre Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter:
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. CoRR abs/2105.01015 (2021) - [i82]Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer:
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. CoRR abs/2105.08541 (2021) - [i81]Noor H. Awad, Neeratyoy Mallik, Frank Hutter:
DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization. CoRR abs/2105.09821 (2021) - [i80]Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer:
Self-Paced Context Evaluation for Contextual Reinforcement Learning. CoRR abs/2106.05110 (2021) - [i79]André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer:
TempoRL: Learning When to Act. CoRR abs/2106.05262 (2021) - [i78]Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka:
Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. CoRR abs/2106.11189 (2021) - [i77]Thomas Elsken, Benedikt Staffler, Arber Zela, Jan Hendrik Metzen, Frank Hutter:
Bag of Tricks for Neural Architecture Search. CoRR abs/2107.03719 (2021) - [i76]Ashwin Raaghav Narayanan, Arber Zela, Tonmoy Saikia, Thomas Brox, Frank Hutter:
Multi-headed Neural Ensemble Search. CoRR abs/2107.04369 (2021) - [i75]Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter:
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. CoRR abs/2109.06716 (2021) - [i74]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter:
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR abs/2109.09831 (2021) - [i73]Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR abs/2110.02102 (2021) - [i72]Shen Yan, Colin White, Yash Savani, Frank Hutter:
NAS-Bench-x11 and the Power of Learning Curves. CoRR abs/2111.03602 (2021) - [i71]Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter:
Transformers Can Do Bayesian Inference. CoRR abs/2112.10510 (2021) - 2020
- [j20]Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Nick Cheney, Patryk Chrabaszcz, Antoine Cully, Stéphane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Léni K. Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David P. Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Schulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Richard A. Watson, Westley Weimer, Jason Yosinski:
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. Artif. Life 26(2): 274-306 (2020) - [j19]Teresa Müller, Milad Miladi, Frank Hutter, Ivo L. Hofacker, Sebastian Will, Rolf Backofen:
The locality dilemma of Sankoff-like RNA alignments. Bioinform. 36(Supplement-1): i242-i250 (2020) - [j18]Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabaszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball:
Machine-learning-based diagnostics of EEG pathology. NeuroImage 220: 117021 (2020) - [c71]Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter:
Meta-Learning of Neural Architectures for Few-Shot Learning. CVPR 2020: 12362-12372 - [c70]André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer:
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. ECAI 2020: 427-434 - [c69]Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter:
Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR 2020 - [c68]Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, Christian Daniel:
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization. ICLR 2020 - [c67]Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter:
Understanding and Robustifying Differentiable Architecture Search. ICLR 2020 - [c66]Arber Zela, Julien Siems, Frank Hutter:
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search. ICLR 2020 - [c65]Gresa Shala, André Biedenkapp, Noor H. Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter:
Learning Step-Size Adaptation in CMA-ES. PPSN (1) 2020: 691-706 - [i70]Arber Zela, Julien Siems, Frank Hutter:
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search. CoRR abs/2001.10422 (2020) - [i69]Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabaszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball:
Machine-Learning-Based Diagnostics of EEG Pathology. CoRR abs/2002.05115 (2020) - [i68]Matilde Gargiani, Andrea Zanelli, Moritz Diehl, Frank Hutter:
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs. CoRR abs/2006.02409 (2020) - [i67]David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer:
Learning Heuristic Selection with Dynamic Algorithm Configuration. CoRR abs/2006.08246 (2020) - [i66]Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh:
Neural Ensemble Search for Performant and Calibrated Predictions. CoRR abs/2006.08573 (2020) - [i65]Lucas Zimmer, Marius Lindauer, Frank Hutter:
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR abs/2006.13799 (2020) - [i64]Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter:
Prior-guided Bayesian Optimization. CoRR abs/2006.14608 (2020) - [i63]Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter:
Auto-Sklearn 2.0: The Next Generation. CoRR abs/2007.04074 (2020) - [i62]Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, Frank Hutter:
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search. CoRR abs/2008.09777 (2020) - [i61]Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter:
Sample-Efficient Automated Deep Reinforcement Learning. CoRR abs/2009.01555 (2020) - [i60]Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter:
Neural Model-based Optimization with Right-Censored Observations. CoRR abs/2009.13828 (2020) - [i59]Jovita Lukasik, David Friede, Arber Zela, Heiner Stuckenschmidt, Frank Hutter, Margret Keuper:
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. CoRR abs/2010.04683 (2020) - [i58]Mauro Vallati, Lukás Chrpa, Thomas Leo McCluskey, Frank Hutter:
On the Importance of Domain Model Configuration for Automated Planning Engines. CoRR abs/2010.07710 (2020) - [i57]Danny Stoll, Jörg K. H. Franke, Diane Wagner, Simon Selg, Frank Hutter:
Hyperparameter Transfer Across Developer Adjustments. CoRR abs/2010.13117 (2020) - [i56]Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter:
Convergence Analysis of Homotopy-SGD for non-convex optimization. CoRR abs/2011.10298 (2020) - [i55]Noor H. Awad, Neeratyoy Mallik, Frank Hutter:
Differential Evolution for Neural Architecture Search. CoRR abs/2012.06400 (2020) - [i54]Noor H. Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter:
Squirrel: A Switching Hyperparameter Optimizer. CoRR abs/2012.08180 (2020)
2010 – 2019
- 2019
- [j17]Katharina Eggensperger, Marius Lindauer, Frank Hutter:
Pitfalls and Best Practices in Algorithm Configuration. J. Artif. Intell. Res. 64: 861-893 (2019) - [j16]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search: A Survey. J. Mach. Learn. Res. 20: 55:1-55:21 (2019) - [c64]Abhinav Sharma, Jan N. van Rijn, Frank Hutter, Andreas Müller:
Hyperparameter Importance for Image Classification by Residual Neural Networks. DS 2019: 112-126 - [c63]Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, Thomas Brox:
AutoDispNet: Improving Disparity Estimation With AutoML. ICCV 2019: 1812-1823 - [c62]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR (Poster) 2019 - [c61]Ilya Loshchilov, Frank Hutter:
Decoupled Weight Decay Regularization. ICLR (Poster) 2019 - [c60]Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter:
Learning to Design RNA. ICLR (Poster) 2019 - [c59]Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter:
NAS-Bench-101: Towards Reproducible Neural Architecture Search. ICML 2019: 7105-7114 - [c58]Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer:
An Evolution Strategy with Progressive Episode Lengths for Playing Games. IJCAI 2019: 1234-1240 - [c57]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier Gonzalez:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS 2019: 6267-6277 - [c56]Louay Abdelgawad, Peter Kluegl, Erdan Genc, Stefan Falkner, Frank Hutter:
Optimizing Neural Networks for Patent Classification. ECML/PKDD (3) 2019: 688-703 - [p7]Matthias Feurer, Frank Hutter:
Hyperparameter Optimization. Automated Machine Learning 2019: 3-33 - [p6]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search. Automated Machine Learning 2019: 63-77 - [p5]Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. Automated Machine Learning 2019: 81-95 - [p4]Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter:
Auto-sklearn: Efficient and Robust Automated Machine Learning. Automated Machine Learning 2019: 113-134 - [p3]Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter:
Towards Automatically-Tuned Deep Neural Networks. Automated Machine Learning 2019: 135-149 - [e4]Frank Hutter, Lars Kotthoff, Joaquin Vanschoren:
Automated Machine Learning - Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning, Springer 2019, ISBN 978-3-030-05317-8 [contents] - [i53]Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter:
NAS-Bench-101: Towards Reproducible Neural Architecture Search. CoRR abs/1902.09635 (2019) - [i52]Michael Volpp, Lukas P. Fröhlich, Andreas Doerr, Frank Hutter, Christian Daniel:
Meta-Learning Acquisition Functions for Bayesian Optimization. CoRR abs/1904.02642 (2019) - [i51]Aaron Klein, Frank Hutter:
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization. CoRR abs/1905.04970 (2019) - [i50]Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, Thomas Brox:
AutoDispNet: Improving Disparity Estimation with AutoML. CoRR abs/1905.07443 (2019) - [i49]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier Gonzalez:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR abs/1905.12982 (2019) - [i48]André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer:
Towards White-box Benchmarks for Algorithm Control. CoRR abs/1906.07644 (2019) - [i47]Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter:
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. CoRR abs/1908.06674 (2019) - [i46]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter:
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. CoRR abs/1908.06756 (2019) - [i45]Marius Lindauer, Frank Hutter:
Best Practices for Scientific Research on Neural Architecture Search. CoRR abs/1909.02453 (2019) - [i44]Raghu Rajan, Frank Hutter:
!MDP Playground: Meta-Features in Reinforcement Learning. CoRR abs/1909.07750 (2019) - [i43]Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter:
Understanding and Robustifying Differentiable Architecture Search. CoRR abs/1909.09656 (2019) - [i42]Matilde Gargiani, Aaron Klein, Stefan Falkner, Frank Hutter:
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings. CoRR abs/1910.04522 (2019) - [i41]Jörg K. H. Franke, Gregor Köhler, Noor H. Awad, Frank Hutter:
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control. CoRR abs/1910.12824 (2019) - [i40]Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter:
OpenML-Python: an extensible Python API for OpenML. CoRR abs/1911.02490 (2019) - [i39]Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter:
Meta-Learning of Neural Architectures for Few-Shot Learning. CoRR abs/1911.11090 (2019) - 2018
- [j15]Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter:
A case study of algorithm selection for the traveling thief problem. J. Heuristics 24(3): 295-320 (2018) - [j14]Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Efficient benchmarking of algorithm configurators via model-based surrogates. Mach. Learn. 107(1): 15-41 (2018) - [c55]Marius Lindauer, Frank Hutter:
Warmstarting of Model-Based Algorithm Configuration. AAAI 2018: 1355-1362 - [c54]