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ORCIDFrank Hutter
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- affiliation: University of Freiburg, Germany
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2020 – today
- 2021
[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] - [i73]Fabio Ferreira, Thomas Nierhoff, Frank Hutter:
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies. CoRR abs/2101.09721 (2021) - [i72]Samuel Müller, André Biedenkapp, Frank Hutter:
In-Loop Meta-Learning with Gradient-Alignment Reward. CoRR abs/2102.03275 (2021) - [i71]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) - 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 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 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]Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox:
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow. ECCV (7) 2018: 677-693 - [c53]Dennis G. Wilson, Silvio Rodrigues, Carlos Segura, Ilya Loshchilov, Frank Hutter, Guillermo López Buenfil, Ahmed Kheiri, Ed Keedwell, Mario Ocampo-Pineda, Ender Özcan, Sergio Iwan Valdez Pea, Brian Goldman, Salvador Botello Rionda, Arturo Hernández Aguirre, Kalyan Veeramachaneni, Sylvain Cussat-Blanc:
Summary of evolutionary computation for wind farm layout optimization. GECCO (Companion) 2018: 31-32 - [c52]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Simple and efficient architecture search for Convolutional Neural Networks. ICLR (Workshop) 2018 - [c51]Stefan Falkner, Aaron Klein, Frank Hutter:
Practical Hyperparameter Optimization for Deep Learning. ICLR (Workshop) 2018 - [c50]Stefan Falkner, Aaron Klein, Frank Hutter:
BOHB: Robust and Efficient Hyperparameter Optimization at Scale. ICML 2018: 1436-1445 - [c49]Benjamin Strang, Peter van der Putten, Jan N. van Rijn, Frank Hutter:
Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML. IDA 2018: 303-315 - [c48]Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter:
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari. IJCAI 2018: 1419-1426 - [c47]Katharina Eggensperger, Marius Lindauer, Frank Hutter:
Neural Networks for Predicting Algorithm Runtime Distributions. IJCAI 2018: 1442-1448 - [c46]Jan N. van Rijn, Frank Hutter:
Hyperparameter Importance Across Datasets. KDD 2018: 2367-2376 - [c45]Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter:
CAVE: Configuration Assessment, Visualization and Evaluation. LION 2018: 115-130 - [c44]James T. Wilson, Frank Hutter, Marc Peter Deisenroth:
Maximizing acquisition functions for Bayesian optimization. NeurIPS 2018: 9906-9917 - [p2]Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Selection and Configuration of Parallel Portfolios. Handbook of Parallel Constraint Reasoning 2018: 583-615 - [i38]Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox:
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. CoRR abs/1802.07095 (2018) - [i37]Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter:
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari. CoRR abs/1802.08842 (2018) - [i36]Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Patryk Chrabaszcz, Nick Cheney, Antoine Cully, Stéphane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni 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 Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard A. Watson, Jason Yosinski:
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. CoRR abs/1803.03453 (2018) - [i35]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Multi-objective Architecture Search for CNNs. CoRR abs/1804.09081 (2018) - [i34]James T. Wilson, Frank Hutter, Marc Peter Deisenroth:
Maximizing acquisition functions for Bayesian optimization. CoRR abs/1805.10196 (2018) - [i33]Robin Tibor Schirrmeister, Patryk Chrabaszcz, Frank Hutter, Tonio Ball:
Generative Reversible Networks. CoRR abs/1806.01610 (2018) - [i32]Stefan Falkner, Aaron Klein, Frank Hutter:
BOHB: Robust and Efficient Hyperparameter Optimization at Scale. CoRR abs/1807.01774 (2018) - [i31]Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter:
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search. CoRR abs/1807.06906 (2018) - [i30]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Neural Architecture Search: A Survey. CoRR abs/1808.05377 (2018) - [i29]Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter:
Learning to Design RNA. CoRR abs/1812.11951 (2018) - 2017
- [j13]Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown:
The Configurable SAT Solver Challenge (CSSC). Artif. Intell. 243: 1-25 (2017) - [j12]Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. 18: 25:1-25:5 (2017) - [c43]Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos:
Efficient Parameter Importance Analysis via Ablation with Surrogates. AAAI 2017: 773-779 - [c42]Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. AISTATS 2017: 528-536 - [c41]Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter:
Learning Curve Prediction with Bayesian Neural Networks. ICLR (Poster) 2017 - [c40]Ilya Loshchilov, Frank Hutter:
SGDR: Stochastic Gradient Descent with Warm Restarts. ICLR (Poster) 2017 - [c39]Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub:
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). IJCAI 2017: 5025-5029 - [c38]Jan N. van Rijn, Frank Hutter:
An Empirical Study of Hyperparameter Importance Across Datasets. AutoML@PKDD/ECML 2017: 91-98 - [e3]Pavel Brazdil, Joaquin Vanschoren, Frank Hutter, Holger H. Hoos:
Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms co-located with the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, AutoML@PKDD/ECML 2017, Skopje, Macedonia, September 22, 2017. CEUR Workshop Proceedings 1998, CEUR-WS.org 2017 [contents] - [i28]Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown, Frank Hutter:
OASC-2017: *Zilla Submission. OASC 2017: 15-18 - [i27]Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball:
Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG. CoRR abs/1703.05051 (2017) - [i26]Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates. CoRR abs/1703.10342 (2017) - [i25]Katharina Eggensperger, Marius Lindauer, Frank Hutter:
Pitfalls and Best Practices in Algorithm Configuration. CoRR abs/1705.06058 (2017) - [i24]Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter:
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets. CoRR abs/1707.08819 (2017) - [i23]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren:
OpenML Benchmarking Suites and the OpenML100. CoRR abs/1708.03731 (2017) - [i22]Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank Hutter, Tonio Ball:
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology. CoRR abs/1708.08012 (2017) - [i21]Marius Lindauer, Frank Hutter:
Warmstarting of Model-based Algorithm Configuration. CoRR abs/1709.04636 (2017) - [i20]Katharina Eggensperger, Marius Lindauer, Frank Hutter:
Predicting Runtime Distributions using Deep Neural Networks. CoRR abs/1709.07615 (2017) - [i19]Jan N. van Rijn, Frank Hutter:
Hyperparameter Importance Across Datasets. CoRR abs/1710.04725 (2017) - [i18]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Simple And Efficient Architecture Search for Convolutional Neural Networks. CoRR abs/1711.04528 (2017) - [i17]Ilya Loshchilov, Frank Hutter:
Fixing Weight Decay Regularization in Adam. CoRR abs/1711.05101 (2017) - [i16]James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc Peter Deisenroth:
The reparameterization trick for acquisition functions. CoRR abs/1712.00424 (2017) - 2016
- [j11]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237: 41-58 (2016) - [j10]Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, Nando de Freitas:
Bayesian Optimization in a Billion Dimensions via Random Embeddings. J. Artif. Intell. Res. 55: 361-387 (2016) - [c37]Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter:
Towards Automatically-Tuned Neural Networks. AutoML@ICML 2016: 58-65 - [c36]Tobias Schubert, Katharina Eggensperger, Alexis Gkogkidis, Frank Hutter, Tonio Ball, Wolfram Burgard:
Automatic bone parameter estimation for skeleton tracking in optical motion capture. ICRA 2016: 5548-5554 - [c35]Marius Lindauer, Rolf-David Bergdoll, Frank Hutter:
An Empirical Study of Per-instance Algorithm Scheduling. LION 2016: 253-259 - [c34]Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter:
Bayesian Optimization with Robust Bayesian Neural Networks. NIPS 2016: 4134-4142 - [e2]Frank Hutter, Lars Kotthoff, Joaquin Vanschoren:
Proceedings of the 2016 Workshop on Automatic Machine Learning, AutoML 2016, co-located with 33rd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, June 24, 2016. JMLR Workshop and Conference Proceedings 64, JMLR.org 2016 [contents] - [i15]Ilya Loshchilov, Frank Hutter:
CMA-ES for Hyperparameter Optimization of Deep Neural Networks. CoRR abs/1604.07269 (2016) - [i14]Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. CoRR abs/1605.07079 (2016) - [i13]Ilya Loshchilov, Frank Hutter:
SGDR: Stochastic Gradient Descent with Restarts. CoRR abs/1608.03983 (2016) - [i12]Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter:
A case study of algorithm selection for the traveling thief problem. CoRR abs/1609.00462 (2016) - [i11]Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter:
Asynchronous Stochastic Gradient MCMC with Elastic Coupling. CoRR abs/1612.00767 (2016) - 2015
- [j9]Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub:
AutoFolio: An Automatically Configured Algorithm Selector. J. Artif. Intell. Res. 53: 745-778 (2015) - [j8]Frank Hutter, Jörg Lücke, Lars Schmidt-Thieme:
Beyond Manual Tuning of Hyperparameters. Künstliche Intell. 29(4): 329-337 (2015) - [c33]Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates. AAAI 2015: 1114-1120 - [c32]