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ORCIDFrank Hutter
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- affiliation: University of Freiburg, Germany
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2020 – today
- 2025
[j36]Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter:
Accurate predictions on small data with a tabular foundation model. Nat. 637(8044): 319-326 (2025)- [i161]Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter:
The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features. CoRR abs/2501.02945 (2025) - [i160]David Salinas, Omar Swelam, Frank Hutter:
Tuning LLM Judges Hyperparameters. CoRR abs/2501.17178 (2025) - 2024
- [j35]Frederic Runge, Jörg K. H. Franke, Daniel Fertmann, Rolf Backofen
, Frank Hutter:
Partial RNA design. Bioinform. 40(Supplement_1): i437-i445 (2024) - [j34]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. J. Artif. Intell. Res. 79: 639-677 (2024) - [j33]Edward Bergman, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger:
AMLTK: A Modular AutoML Toolkit in Python. J. Open Source Softw. 9(100): 6367 (2024) - [c127]Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor H. Awad:
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes. AIES (1) 2024: 1231-1242 - [c126]Carl Hvarfner, Frank Hutter, Luigi Nardi:
A General Framework for User-Guided Bayesian Optimization. ICLR 2024 - [c125]Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka:
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. ICLR 2024 - [c124]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. ICML 2024 - [c123]Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl:
Position: A Call to Action for a Human-Centered AutoML Paradigm. ICML 2024 - [c122]Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman, Frank Hutter:
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. ICML 2024 - [c121]Benjamin Feuer, Robin Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White:
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. NeurIPS 2024 - [c120]Jörg K. H. Franke, Michael Hefenbrock, Gregor Köhler, Frank Hutter:
Improving Deep Learning Optimization through Constrained Parameter Regularization. NeurIPS 2024 - [c119]Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter:
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. NeurIPS 2024 - [c118]Rhea Sukthanker, Arber Zela, Benedikt Staffler, Aaron Klein, Lennart Purucker, Jörg K. H. Franke, Frank Hutter:
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models. NeurIPS 2024 - [i159]Frederic Runge, Jörg K. H. Franke, Daniel Fertmann, Frank Hutter:
Rethinking Performance Measures of RNA Secondary Structure Problems. CoRR abs/2401.05351 (2024) - [i158]Riccardo Grazzi, Julien Siems, Simon Schrodi, Thomas Brox, Frank Hutter:
Is Mamba Capable of In-Context Learning? CoRR abs/2402.03170 (2024) - [i157]Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White:
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. CoRR abs/2402.11137 (2024) - [i156]Bedionita Soro, Bruno Andreis, Hayeon Lee, Song Chong, Frank Hutter, Sung Ju Hwang:
Diffusion-based Neural Network Weights Generation. CoRR abs/2402.18153 (2024) - [i155]Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley, Josif Grabocka, Frank Hutter:
Multi-objective Differentiable Neural Architecture Search. CoRR abs/2402.18213 (2024) - [i154]Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter:
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks. CoRR abs/2403.01888 (2024) - [i153]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. CoRR abs/2404.16551 (2024) - [i152]Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Edward Bergman, Frank Hutter:
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. CoRR abs/2404.16795 (2024) - [i151]Edward Bergman, Lennart Purucker, Frank Hutter:
Don't Waste Your Time: Early Stopping Cross-Validation. CoRR abs/2405.03389 (2024) - [i150]Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Jörg K. H. Franke, Frank Hutter:
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models. CoRR abs/2405.10299 (2024) - [i149]Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl:
Position: A Call to Action for a Human-Centered AutoML Paradigm. CoRR abs/2406.03348 (2024) - [i148]Simon Blauth, Tobias Bürger, Zacharias Häringer, Jörg K. H. Franke, Frank Hutter:
Fast Optimizer Benchmark. CoRR abs/2406.18701 (2024) - [i147]Jake Robertson, Noah Hollmann, Noor H. Awad, Frank Hutter:
FairPFN: Transformers Can do Counterfactual Fairness. CoRR abs/2407.05732 (2024) - [i146]Anton Geburek, Neeratyoy Mallik, Danny Stoll, Xavier Bouthillier, Frank Hutter:
LMEMs for post-hoc analysis of HPO Benchmarking. CoRR abs/2408.02533 (2024) - [i145]Lukas Strack, Mahmoud Safari, Frank Hutter:
Efficient Search for Customized Activation Functions with Gradient Descent. CoRR abs/2408.06820 (2024) - [i144]Fabio Ferreira, Moreno Schlageter, Raghu Rajan, Andre Biedenkapp, Frank Hutter:
One-shot World Models Using a Transformer Trained on a Synthetic Prior. CoRR abs/2409.14084 (2024) - [i143]Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer:
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR abs/2409.18827 (2024) - [i142]Samuel Müller, Noah Hollmann, Frank Hutter:
Bayes' Power for Explaining In-Context Learning Generalizations. CoRR abs/2410.01565 (2024) - [i141]Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka:
Dynamic Post-Hoc Neural Ensemblers. CoRR abs/2410.04520 (2024) - [i140]Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter:
GAMformer: In-Context Learning for Generalized Additive Models. CoRR abs/2410.04560 (2024) - [i139]Rhea Sanjay Sukthanker, Benedikt Staffler, Frank Hutter, Aaron Klein:
LLM Compression with Neural Architecture Search. CoRR abs/2410.06479 (2024) - [i138]Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems, David Salinas, Frank Hutter:
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models. CoRR abs/2410.09385 (2024) - [i137]Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor H. Awad:
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes. CoRR abs/2410.13286 (2024) - [i136]Jaris Küken, Lennart Purucker, Frank Hutter:
Large Language Models Engineer Too Many Simple Features For Tabular Data. CoRR abs/2410.17787 (2024) - [i135]Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka:
Ensembling Finetuned Language Models for Text Classification. CoRR abs/2410.19889 (2024) - [i134]Tobias Strangmann, Lennart Purucker, Jörg K. H. Franke, Ivo Rapant, Fabio Ferreira, Frank Hutter:
Transfer Learning for Finetuning Large Language Models. CoRR abs/2411.01195 (2024) - [i133]Neeratyoy Mallik, Maciej Janowski, Johannes Hog, Herilalaina Rakotoarison, Aaron Klein, Josif Grabocka, Frank Hutter:
Warmstarting for Scaling Language Models. CoRR abs/2411.07340 (2024) - [i132]Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter:
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. CoRR abs/2411.10634 (2024) - [i131]Riccardo Grazzi, Julien Siems, Jörg K. H. Franke, Arber Zela, Frank Hutter, Massimiliano Pontil:
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. CoRR abs/2411.12537 (2024) - 2023
- [j32]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) - [j31]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) - [j30]Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer:
Contextualize Me - The Case for Context in Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (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) - [c117]Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter:
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. ICLR 2023 - [c116]Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka:
Gray-Box Gaussian Processes for Automated Reinforcement Learning. ICLR 2023 - [c115]Gresa Shala, Thomas Elsken, Frank Hutter, Josif Grabocka:
Transfer NAS with Meta-learned Bayesian Surrogates. ICLR 2023 - [c114]Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter:
PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023: 25444-25470 - [c113]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 - [c112]Shuhei Watanabe, Frank Hutter:
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization. IJCAI 2023: 4371-4379 - [c111]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 - [c110]Shuhei Watanabe, Archit Bansal, Frank Hutter:
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. IJCAI 2023: 4389-4396 - [c109]Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter:
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. NeurIPS 2023 - [c108]Samuel Dooley, Rhea Sukthanker, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum:
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition. NeurIPS 2023 - [c107]Noah Hollmann, Samuel Müller, Frank Hutter:
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. NeurIPS 2023 - [c106]Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi:
Self-Correcting Bayesian Optimization through Bayesian Active Learning. NeurIPS 2023 - [c105]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. NeurIPS 2023 - [c104]Simon Schrodi, Danny Stoll, Binxin Ru, Rhea Sukthanker, Thomas Brox, Frank Hutter:
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars. NeurIPS 2023 - [e9]Aleksandra Faust, Roman Garnett, Colin White, Frank Hutter, Jacob R. Gardner:
International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, Germany. Proceedings of Machine Learning Research 224, PMLR 2023 [contents] - [i130]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) - [i129]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) - [i128]Shuhei Watanabe, Archit Bansal, Frank Hutter:
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. CoRR abs/2304.10255 (2023) - [i127]Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi:
Self-Correcting Bayesian Optimization through Bayesian Active Learning. CoRR abs/2304.11005 (2023) - [i126]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) - [i125]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) - [i124]Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter:
PFNs Are Flexible Models for Real-World Bayesian Optimization. CoRR abs/2305.17535 (2023) - [i123]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) - [i122]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) - [i121]Frederic Runge, Jörg K. H. Franke, Frank Hutter:
Towards Automated Design of Riboswitches. CoRR abs/2307.08801 (2023) - [i120]Jörg K. H. Franke, Frederic Runge, Frank Hutter:
Scalable Deep Learning for RNA Secondary Structure Prediction. CoRR abs/2307.10073 (2023) - [i119]Fabio Ferreira, Ivo Rapant, Frank Hutter:
Hard View Selection for Contrastive Learning. CoRR abs/2310.03940 (2023) - [i118]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 A. 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) - [i117]Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter:
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. CoRR abs/2310.20447 (2023) - [i116]Jörg K. H. Franke, Michael Hefenbrock, Gregor Köhler, Frank Hutter:
New Horizons in Parameter Regularization: A Constraint Approach. CoRR abs/2311.09058 (2023) - [i115]Carl Hvarfner, Frank Hutter, Luigi Nardi:
A General Framework for User-Guided Bayesian Optimization. CoRR abs/2311.14645 (2023) - [i114]Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter:
Weight-Entanglement Meets Gradient-Based Neural Architecture Search. CoRR abs/2312.10440 (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) - [c103]André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr:
Theory-inspired parameter control benchmarks for dynamic algorithm configuration. GECCO 2022: 766-775 - [c102]Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter:
Transformers Can Do Bayesian Inference. ICLR 2022 - [c101]Fabio Ferreira, Thomas Nierhoff, Andreas Sälinger, Frank Hutter:
Learning Synthetic Environments and Reward Networks for Reinforcement Learning. ICLR 2022 - [c100]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 - [c99]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 - [c98]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 - [c97]Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter:
Zero-shot AutoML with Pretrained Models. ICML 2022: 17138-17155 - [c96]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 - [c95]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 - [c94]Jörg K. H. Franke, Frederic Runge, Frank Hutter:
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design. NeurIPS 2022 - [c93]Carl Hvarfner, Frank Hutter, Luigi Nardi:
Joint Entropy Search For Maximally-Informed Bayesian Optimization. NeurIPS 2022 - [c92]Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter:
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. NeurIPS 2022 - [c91]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) - [c90]David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer:
Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS 2021: 597-605 - [c89]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 - [c88]Samuel G. Müller, Frank Hutter:
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. ICCV 2021: 754-762 - [c87]Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter:
Sample-Efficient Automated Deep Reinforcement Learning. ICLR 2021 - [c86]André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer:
TempoRL: Learning When to Act. ICML 2021: 914-924 - [c85]Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer:
Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML 2021: 2948-2958 - [c84]Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer:
DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI 2021: 1668-1674 - [c83]Noor H. Awad, Neeratyoy Mallik, Frank Hutter:
DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization. IJCAI 2021: 2147-2153 - [c82]Jovita Lukasik, David Friede, Arber Zela, Frank Hutter, Margret Keuper:
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. IJCNN 2021: 1-8 - [c81]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 - [c80]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 - [c79]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 - [c78]Nicholas Roberts, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, Shuaicheng Niu, Jianyu Heng, Hongyang Qin, Minwen Deng, Johannes Hog, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks:
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) 2021: 151-170 - [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:
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) - [c72]Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter:
Meta-Learning of Neural Architectures for Few-Shot Learning. CVPR 2020: 12362-12372 - [c71]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 - [c70]Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter:
Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR 2020 - [c69]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 - [c68]Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter:
Understanding and Robustifying Differentiable Architecture Search. ICLR 2020 - [c67]Arber Zela, Julien Siems, Frank Hutter:
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search. ICLR 2020 - [c66]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) - [c65]Abhinav Sharma, Jan N. van Rijn, Frank Hutter, Andreas Müller:
Hyperparameter Importance for Image Classification by Residual Neural Networks. DS 2019: 112-126 - [c64]Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, Thomas Brox:
AutoDispNet: Improving Disparity Estimation With AutoML. ICCV 2019: 1812-1823 - [c63]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR (Poster) 2019 - [c62]Ilya Loshchilov, Frank Hutter:
Decoupled Weight Decay Regularization. ICLR (Poster) 2019 - [c61]Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter:
Learning to Design RNA. ICLR (Poster) 2019 - [c60]Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter:
NAS-Bench-101: Towards Reproducible Neural Architecture Search. ICML 2019: 7105-7114 - [c59]Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer:
An Evolution Strategy with Progressive Episode Lengths for Playing Games. IJCAI 2019: 1234-1240 - [c58]Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS 2019: 6267-6277 - [c57]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 González:
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) - [c56]Marius Lindauer, Frank Hutter:
Warmstarting of Model-Based Algorithm Configuration. AAAI 2018: 1355-1362 - [c55]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 - [c54]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 - [c53]Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Simple and efficient architecture search for Convolutional Neural Networks. ICLR (Workshop) 2018 - [c52]Stefan Falkner, Aaron Klein, Frank Hutter:
Practical Hyperparameter Optimization for Deep Learning. ICLR (Workshop) 2018 - [c51]Stefan Falkner, Aaron Klein, Frank Hutter:
BOHB: Robust and Efficient Hyperparameter Optimization at Scale. ICML 2018: 1436-1445 - [c50]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 - [c49]Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter:
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari. IJCAI 2018: 1419-1426 - [c48]Katharina Eggensperger, Marius Lindauer, Frank Hutter:
Neural Networks for Predicting Algorithm Runtime Distributions. IJCAI 2018: 1442-1448 - [c47]Jan N. van Rijn, Frank Hutter:
Hyperparameter Importance Across Datasets. KDD 2018: 2367-2376 - [c46]Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter:
CAVE: Configuration Assessment, Visualization and Evaluation. LION 2018: 115-130 - [c45]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 Schulte, 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) - [c44]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 - [c43]Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter:
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. AISTATS 2017: 528-536 - [c42]Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter:
Learning Curve Prediction with Bayesian Neural Networks. ICLR (Poster) 2017 - [c41]Ilya Loshchilov, Frank Hutter:
SGDR: Stochastic Gradient Descent with Warm Restarts. ICLR (Poster) 2017 - [c40]Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub:
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). IJCAI 2017: 5025-5029 - [c39]Jan N. van Rijn, Frank Hutter:
An Empirical Study of Hyperparameter Importance Across Datasets. AutoML@PKDD/ECML 2017: 91-98 - [c38]Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, Jürgen Schmidhuber:
The Sacred Infrastructure for Computational Research. SciPy 2017: 49-56 - [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]Matthias Feurer, Jost Tobias Springenberg, Frank Hutter:
Initializing Bayesian Hyperparameter Optimization via Meta-Learning. AAAI 2015: 1128-1135 - [c31]Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub:
AutoFolio: Algorithm Configuration for Algorithm Selection. AAAI Workshop: Algorithm Configuration 2015 - [c30]Jendrik Seipp, Silvan Sievers, Malte Helmert, Frank Hutter:
Automatic Configuration of Sequential Planning Portfolios. AAAI 2015: 3364-3370 - [c29]Mauro Vallati, Frank Hutter, Lukás Chrpa, Thomas Leo McCluskey:
On the Effective Configuration of Planning Domain Models. IJCAI 2015: 1704-1711 - [c28]Tobias Domhan, Jost Tobias Springenberg, Frank Hutter:
Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves. IJCAI 2015: 3460-3468 - [c27]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract). IJCAI 2015: 4197-4201 - [c26]Marius Lindauer, Holger H. Hoos, Frank Hutter:
From Sequential Algorithm Selection to Parallel Portfolio Selection. LION 2015: 1-16 - [c25]Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter:
Efficient and Robust Automated Machine Learning. NIPS 2015: 2962-2970 - [c24]Stefan Falkner, Marius Lindauer, Frank Hutter:
SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. SAT 2015: 215-222 - [e1]Frank Hutter, Marius Lindauer, Yuri Malitsky:
Algorithm Configuration, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015. AAAI Technical Report WS-15-01, AAAI Press 2015, ISBN 978-1-57735-712-4 [contents] - [i10]Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown:
The Configurable SAT Solver Challenge (CSSC). CoRR abs/1505.01221 (2015) - [i9]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. CoRR abs/1506.02465 (2015) - [i8]Ilya Loshchilov, Frank Hutter:
Online Batch Selection for Faster Training of Neural Networks. CoRR abs/1511.06343 (2015) - 2014
- [j7]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm runtime prediction: Methods & evaluation. Artif. Intell. 206: 79-111 (2014) - [j6]Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, Lin Xu:
Understanding the empirical hardness of NP-complete problems. Commun. ACM 57(5): 98-107 (2014) - [c23]Chris Fawcett, Mauro Vallati, Frank Hutter, Jörg Hoffmann, Holger H. Hoos, Kevin Leyton-Brown:
Improved Features for Runtime Prediction of Domain-Independent Planners. ICAPS 2014 - [c22]Frank Hutter:
Bayesian Optimization for More Automatic Machine Learning. MetaSel@ECAI 2014: 2 - [c21]Matthias Feurer, Jost Tobias Springenberg, Frank Hutter:
Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters. MetaSel@ECAI 2014: 3-10 - [c20]Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Surrogate Benchmarks for Hyperparameter Optimization. MetaSel@ECAI 2014: 24-31 - [c19]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
An Efficient Approach for Assessing Hyperparameter Importance. ICML 2014: 754-762 - [c18]Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle:
AClib: A Benchmark Library for Algorithm Configuration. LION 2014: 36-40 - [c17]Daniel Geschwender, Frank Hutter, Lars Kotthoff, Yuri Malitsky, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Configuration in the Cloud: A Feasibility Study. LION 2014: 41-46 - [i7]Frank Hutter, Thomas Stützle, Kevin Leyton-Brown, Holger H. Hoos:
ParamILS: An Automatic Algorithm Configuration Framework. CoRR abs/1401.3492 (2014) - 2013
- [c16]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
An evaluation of sequential model-based optimization for expensive blackbox functions. GECCO (Companion) 2013: 1209-1216 - [c15]Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas:
Bayesian Optimization in High Dimensions via Random Embeddings. IJCAI 2013: 1778-1784 - [c14]Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. KDD 2013: 847-855 - [c13]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Identifying Key Algorithm Parameters and Instance Features Using Forward Selection. LION 2013: 364-381 - [i6]Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas:
Bayesian Optimization in a Billion Dimensions via Random Embeddings. CoRR abs/1301.1942 (2013) - [i5]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Bayesian Optimization With Censored Response Data. CoRR abs/1310.1947 (2013) - [i4]Frank Hutter, Michael A. Osborne:
A Kernel for Hierarchical Parameter Spaces. CoRR abs/1310.5738 (2013) - 2012
- [c12]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Parallel Algorithm Configuration. LION 2012: 55-70 - [c11]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors. SAT 2012: 228-241 - [i3]Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms. CoRR abs/1208.3719 (2012) - [i2]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Runtime Prediction: The State of the Art. CoRR abs/1211.0906 (2012) - 2011
- [c10]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Sequential Model-Based Optimization for General Algorithm Configuration. LION 2011: 507-523 - [i1]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
SATzilla: Portfolio-based Algorithm Selection for SAT. CoRR abs/1111.2249 (2011) - 2010
- [j5]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Tradeoffs in the empirical evaluation of competing algorithm designs. Ann. Math. Artif. Intell. 60(1-2): 65-89 (2010) - [c9]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Automated Configuration of Mixed Integer Programming Solvers. CPAIOR 2010: 186-202 - [c8]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy:
Time-Bounded Sequential Parameter Optimization. LION 2010: 281-298 - [p1]Frank Hutter, Thomas Bartz-Beielstein, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy:
Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches. Experimental Methods for the Analysis of Optimization Algorithms 2010: 363-414
2000 – 2009
- 2009
- [j4]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle:
ParamILS: An Automatic Algorithm Configuration Framework. J. Artif. Intell. Res. 36: 267-306 (2009) - [c7]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy:
An experimental investigation of model-based parameter optimisation: SPO and beyond. GECCO 2009: 271-278 - 2008
- [j3]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
SATzilla: Portfolio-based Algorithm Selection for SAT. J. Artif. Intell. Res. 32: 565-606 (2008) - 2007
- [c6]Frank Hutter, Holger H. Hoos, Thomas Stützle:
Automatic Algorithm Configuration Based on Local Search. AAAI 2007: 1152-1157 - [c5]Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
: The Design and Analysis of an Algorithm Portfolio for SAT. CP 2007: 712-727 - [c4]Frank Hutter, Domagoj Babic, Holger H. Hoos, Alan J. Hu:
Boosting Verification by Automatic Tuning of Decision Procedures. FMCAD 2007: 27-34 - 2006
- [j2]Wolfgang Achtner, Esma Aïmeur, Sarabjot Singh Anand, Douglas E. Appelt, Naveen Ashish, Tiffany Barnes, Joseph E. Beck, M. Bernardine Dias, Prashant Doshi, Chris Drummond, William Elazmeh, Ariel Felner, Dayne Freitag, Hector Geffner, Christopher W. Geib, Richard Goodwin, Robert C. Holte, Frank Hutter, Fair Isaac, Nathalie Japkowicz, Gal A. Kaminka, Sven Koenig, Michail G. Lagoudakis, David B. Leake, Lundy Lewis, Hugo Liu, Ted Metzler, Rada Mihalcea, Bamshad Mobasher, Pascal Poupart, David V. Pynadath, Thomas Roth-Berghofer, Wheeler Ruml, Stefan Schulz, Sven Schwarz, Stephanie Seneff, Amit P. Sheth, Ron Sun, Michael Thielscher, Afzal Upal, Jason D. Williams, Steve J. Young, Dmitry Zelenko:
Reports on the Twenty-First National Conference on Artificial Intelligence (AAAI-06) Workshop Program. AI Mag. 27(4): 92-102 (2006) - [c3]Frank Hutter, Youssef Hamadi, Holger H. Hoos, Kevin Leyton-Brown:
Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. CP 2006: 213-228 - 2005
- [c2]Frank Hutter, Holger H. Hoos, Thomas Stützle:
Efficient Stochastic Local Search for MPE Solving. IJCAI 2005: 169-174 - 2004
- [j1]Nando de Freitas, Richard Dearden, Frank Hutter, Rubén Morales-Menéndez, Jim Mutch, David Poole:
Diagnosis by a waiter and a Mars explorer. Proc. IEEE 92(3): 455-468 (2004) - 2002
- [c1]Frank Hutter, Dave A. D. Tompkins, Holger H. Hoos:
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT. CP 2002: 233-248
Coauthor Index
André Biedenkapp
aka: Andre Biedenkapp
aka: Andre Biedenkapp
[j33] [c123] [i149] [i143] [j30] [j29] [c105] [i122] [j28] [j27] [j26] [j25] [c100] [c91] [e8] [i113] [i112] [i108] [i105] [i104] [i103] [i101] [j22] [j21] [c90] [c86] [c85] [c84] [c79] [c75] [c73] [i83] [i82] [i80] [i79] [i78] [i75] [i74] [i73] [c71] [c66] [i67] [i65] [i64] [i63] [i60] [i54] [j17] [c59] [p3] [i48] [i47] [i46] [i45] [j15] [j14] [c56] [c48] [c46] [p2] [j13] [c44] [c40] [i26] [i25] [i21] [i20] [j11] [c35] [i12] [j9] [c31] [c26] [c24] [e1] [i10] [i9] [c18]
Samuel Müller 0005
aka: Samuel G. Müller
aka: Samuel G. Müller
Robin Schirrmeister
aka: Robin Tibor Schirrmeister
aka: Robin Tibor Schirrmeister
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