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Frank 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) - 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) - [c126]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 - [c125]Carl Hvarfner, Frank Hutter, Luigi Nardi:
A General Framework for User-Guided Bayesian Optimization. ICLR 2024 - [c124]Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka:
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. ICLR 2024 - [c123]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. ICML 2024 - [c122]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 - [c121]Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman, Frank Hutter:
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. ICML 2024 - [c120]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 - [c119]Jörg K. H. Franke, Michael Hefenbrock, Gregor Köhler, Frank Hutter:
Improving Deep Learning Optimization through Constrained Parameter Regularization. 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]