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- affiliation: University of Freiburg, Germany
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2020 – today
- 2023
[j31]Rohit Mohan, 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. Int. J. Comput. Vis. 131(7): 1784-1807 (2023)- [j30]Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp
, Jan Ole von Hartz, Frank Hutter:
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning. J. Artif. Intell. Res. 77: 821-890 (2023) - [j29]Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer:
MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Trans. Mach. Learn. Res. 2023 (2023) - [c110]Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter:
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. ICLR 2023 - [c109]Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka:
Gray-Box Gaussian Processes for Automated Reinforcement Learning. ICLR 2023 - [c108]Gresa Shala, Thomas Elsken, Frank Hutter, Josif Grabocka:
Transfer NAS with Meta-learned Bayesian Surrogates. ICLR 2023 - [c107]Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter:
PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023: 25444-25470 - [c106]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA 2023: 130-142 - [c105]Shuhei Watanabe, Frank Hutter:
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization. IJCAI 2023: 4371-4379 - [c104]Shuhei Watanabe, Noor H. Awad, Masaki Onishi, Frank Hutter:
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator. IJCAI 2023: 4380-4388 - [c103]Shuhei Watanabe, Archit Bansal, Frank Hutter:
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. IJCAI 2023: 4389-4396 - [i128]Colin White, Mahmoud Safari, Rhea Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, Frank Hutter:
Neural Architecture Search: Insights from 1000 Papers. CoRR abs/2301.08727 (2023) - [i127]Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR abs/2303.08485 (2023) - [i126]Shuhei Watanabe, Archit Bansal, Frank Hutter:
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. CoRR abs/2304.10255 (2023) - [i125]Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi:
Self-Correcting Bayesian Optimization through Bayesian Active Learning. CoRR abs/2304.11005 (2023) - [i124]Noah Hollmann, Samuel Müller, Frank Hutter:
LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. CoRR abs/2305.03403 (2023) - [i123]Noor H. Awad, Ayushi Sharma, Philipp Muller, Janek Thomas, Frank Hutter:
MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization. CoRR abs/2305.04502 (2023) - [i122]Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter:
PFNs Are Flexible Models for Real-World Bayesian Optimization. CoRR abs/2305.17535 (2023) - [i121]Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka:
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. CoRR abs/2306.03828 (2023) - [i120]Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter:
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. CoRR abs/2306.12370 (2023) - [i119]Frederic Runge, Jörg K. H. Franke, Frank Hutter:
Towards Automated Design of Riboswitches. CoRR abs/2307.08801 (2023) - [i118]Jörg K. H. Franke, Frederic Runge, Frank Hutter:
Scalable Deep Learning for RNA Secondary Structure Prediction. CoRR abs/2307.10073 (2023) - [i117]Fabio Ferreira, Ivo Rapant, Frank Hutter:
Hard View Selection for Contrastive Learning. CoRR abs/2310.03940 (2023) - [i116]Yoshua Bengio, Geoffrey E. Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian K. Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca D. Dragan, Philip H. S. Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann:
Managing AI Risks in an Era of Rapid Progress. CoRR abs/2310.17688 (2023) - [i115]Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter:
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. CoRR abs/2310.20447 (2023) - [i114]Jörg K. H. Franke, Michael Hefenbrock, Gregor Köhler, Frank Hutter:
New Horizons in Parameter Regularization: A Constraint Approach. CoRR abs/2311.09058 (2023) - 2022
- [j28]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) - [j27]Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter:
Automated Dynamic Algorithm Configuration. J. Artif. Intell. Res. 75: 1633-1699 (2022) - [j26]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) - [j25]Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter:
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. J. Mach. Learn. Res. 23: 261:1-261:61 (2022) - [c102]André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr:
Theory-inspired parameter control benchmarks for dynamic algorithm configuration. GECCO 2022: 766-775 - [c101]Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter:
Transformers Can Do Bayesian Inference. ICLR 2022 - [c100]Fabio Ferreira, Thomas Nierhoff, Andreas Sälinger, Frank Hutter:
Learning Synthetic Environments and Reward Networks for Reinforcement Learning. ICLR 2022 - [c99]Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi:
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. ICLR 2022 - [c98]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. ICLR 2022 - [c97]Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, Frank Hutter:
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks. ICLR 2022 - [c96]Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter:
Zero-shot AutoML with Pretrained Models. ICML 2022: 17138-17155 - [c95]Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad, Raghu Rajan, Frank Hutter, Wolfram Burgard:
T3VIP: Transformation-based 3D Video Prediction. IROS 2022: 4174-4181 - [c94]Archit Bansal, Danny Stoll, Maciej Janowski, Arber Zela, Frank Hutter:
JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search. NeurIPS 2022 - [c93]Jörg K. H. Franke, Frederic Runge, Frank Hutter:
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design. NeurIPS 2022 - [c92]Carl Hvarfner, Frank Hutter, Luigi Nardi:
Joint Entropy Search For Maximally-Informed Bayesian Optimization. NeurIPS 2022 - [c91]Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter:
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. NeurIPS 2022 - [c90]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) 2022: 664-680 - [e8]Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett:
International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. Proceedings of Machine Learning Research 188, PMLR 2022 [contents] - [i113]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) - [i112]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) - [i111]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) - [i110]Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter:
Learning Synthetic Environments and Reward Networks for Reinforcement Learning. CoRR abs/2202.02790 (2022) - [i109]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) - [i108]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) - [i107]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) - [i106]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) - [i105]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) - [i104]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. CoRR abs/2205.05511 (2022) - [i103]Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter:
Automated Dynamic Algorithm Configuration. CoRR abs/2205.13881 (2022) - [i102]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) - [i101]René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer:
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. CoRR abs/2206.03493 (2022) - [i100]Carl Hvarfner, Frank Hutter, Luigi Nardi:
Joint Entropy Search For Maximally-Informed Bayesian Optimization. CoRR abs/2206.04771 (2022) - [i99]Adrian El Baz, André C. P. L. F. de 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) - [i98]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) - [i97]Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter:
Meta-Learning a Real-Time Tabular AutoML Method For Small Data. CoRR abs/2207.01848 (2022) - [i96]Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter:
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning. CoRR abs/2207.07875 (2022) - [i95]Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad, Raghu Rajan, Frank Hutter, Wolfram Burgard:
T3VIP: Transformation-based 3D Video Prediction. CoRR abs/2209.11693 (2022) - [i94]Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter:
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. CoRR abs/2210.03230 (2022) - [i93]Rhea Sukthanker, Samuel Dooley, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum:
On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition. CoRR abs/2210.09943 (2022) - [i92]Simon Schrodi, Danny Stoll, Binxin Ru, Rhea Sukthanker, Thomas Brox, Frank Hutter:
Towards Discovering Neural Architectures from Scratch. CoRR abs/2211.01842 (2022) - [i91]Shuhei Watanabe, Frank Hutter:
c-TPE: Generalizing Tree-structured Parzen Estimator with Inequality Constraints for Continuous and Categorical Hyperparameter Optimization. CoRR abs/2211.14411 (2022) - [i90]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR abs/2212.04183 (2022) - [i89]Shuhei Watanabe, Noor H. Awad, Masaki Onishi, Frank Hutter:
Multi-objective Tree-structured Parzen Estimator Meets Meta-learning. CoRR abs/2212.06751 (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) - [c89]David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer:
Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS 2021: 597-605 - [c88]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 - [c87]Samuel G. Müller, Frank Hutter:
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. ICCV 2021: 754-762 - [c86]Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter:
Sample-Efficient Automated Deep Reinforcement Learning. ICLR 2021 - [c85]André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer:
TempoRL: Learning When to Act. ICML 2021: 914-924 - [c84]Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer:
Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML 2021: 2948-2958 - [c83]Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer:
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI 2021: 1668-1674 - [c82]Noor H. Awad, Neeratyoy Mallik, Frank Hutter:
DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization. IJCAI 2021: 2147-2153 - [c81]Jovita Lukasik, David Friede, Arber Zela, Frank Hutter, Margret Keuper:
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. IJCNN 2021: 1-8 - [c80]Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. de Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy M. Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan N. van Rijn, Haozhe Sun, Xin Wang, Wenwu Zhu:
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. NeurIPS (Competition and Demos) 2021: 80-96 - [c79]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 - [c78]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 - [c77]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 - [c76]Shen Yan, Colin White, Yash Savani, Frank Hutter:
NAS-Bench-x11 and the Power of Learning Curves. NeurIPS 2021: 22534-22549 - [c75]Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka:
Well-tuned Simple Nets Excel on Tabular Datasets. NeurIPS 2021: 23928-23941 - [c74]Colin White, Arber Zela, Robin Ru, Yang Liu, Frank Hutter:
How Powerful are Performance Predictors in Neural Architecture Search? NeurIPS 2021: 28454-28469 - [c73]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:
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