Frank Hutter - RankMe

403 papers A* 81A 11B 6Journal 240Unranked 48

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. J. Data-centric Mach. Learn. Res. Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger Hoos, Marius Lindauer, Theresa Eimer
2026	J	`jnl`	Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch. CoRR Fabio Ferreira, Lucca Wobbe, Arjun Krishnakumar, Frank Hutter, Arber Zela
2026	J	`jnl`	Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models. CoRR Magnus Bühler, Lennart Purucker, Frank Hutter
2026	J	`jnl`	Multi-Objective Hierarchical Optimization with Large Language Models. CoRR Andrej Schwanke, Lyubomir Ivanov, David Salinas, Frank Hutter, Arber Zela
2026	J	`jnl`	Use What You Know: Causal Foundation Models with Partial Graphs. CoRR Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Schölkopf
2025	J	`jnl`	A Llama walks into the 'Bar': Efficient Supervised Fine-Tuning for Legal Reasoning in the Multi-state Bar Exam. CoRR Rean Fernandes, André Biedenkapp, Frank Hutter, Noor H. Awad
2025	J	`jnl`	Accurate predictions on small data with a tabular foundation model. Nat. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter
2025	J	`jnl`	Agentic NL2SQL to Reduce Computational Costs. CoRR Dominik Jehle, Lennart Purucker, Frank Hutter
2025	J	`jnl`	Balancing Synthetic Data and Replay for Enhancing Task-Specific Capabilities. CoRR Urs Spiegelhalter, Jörg K. H. Franke, Frank Hutter
2025	A*	`conf`	Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks. ICML Dongwoo Lee, Dong Bok Lee, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Frank Hutter, Seon Joo Kim, Hae Beom Lee
2025	J	`jnl`	Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks. CoRR Dongwoo Lee, Dong Bok Lee, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Frank Hutter, Seon Joo Kim, Hae Beom Lee
2025	A*	`conf`	Beyond Random Augmentations: Pretraining with Hard Views. ICLR Fabio Ferreira, Ivo Rapant, Jörg K. H. Franke, Frank Hutter
2025	J	`jnl`	Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182). Dagstuhl Reports Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, Frank Hutter
2025	J	`jnl`	DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning. CoRR Sarah Segel, Helena Graf, Edward Bergman, Kristina Thieme, Marcel Wever, Alexander Tornede, Frank Hutter, Marius Lindauer
2025	J	`jnl`	DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning. J. Mach. Learn. Res. Sarah Segel, Helena Graf, Edward Bergman, Kristina Thieme, Marcel Wever, Alexander Tornede, Frank Hutter, Marius Lindauer
2025	J	`jnl`	DeltaProduct: Increasing the Expressivity of DeltaNet Through Products of Householders. CoRR Julien Siems, Timur Carstensen, Arber Zela, Frank Hutter, Massimiliano Pontil, Riccardo Grazzi
2025	A*	`conf`	Diffusion-based Neural Network Weights Generation. ICLR Bedionita Soro, Bruno Andreis, Hayeon Lee, Wonyong Jeong, Song Chong, Frank Hutter, Sung Ju Hwang
2025	J	`jnl`	Do-PFN: In-Context Learning for Causal Effect Estimation. CoRR Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Schölkopf
2025	J	`jnl`	Does TabPFN Understand Causal Structures? CoRR Omar Swelam, Lennart Purucker, Jake Robertson, Hanne Raum, Joschka Boedecker, Frank Hutter
2025	J	`jnl`	Early Stopping Tabular In-Context Learning. CoRR Jaris Küken, Lennart Purucker, Frank Hutter
2025	J	`jnl`	EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks. CoRR Michael Arbel, David Salinas, Frank Hutter
2025	A*	`conf`	FairPFN: A Tabular Foundation Model for Causal Fairness. ICML Jake Robertson, Noah Hollmann, Samuel Müller, Noor H. Awad, Frank Hutter
2025	J	`jnl`	FairPFN: A Tabular Foundation Model for Causal Fairness. CoRR Jake Robertson, Noah Hollmann, Samuel Müller, Noor H. Awad, Frank Hutter
2025	—	`conf`	Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization. AutoML Timur Carstensen, Neeratyoy Mallik, Frank Hutter, Martin Rapp
2025	J	`jnl`	Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization. CoRR Timur Carstensen, Neeratyoy Mallik, Frank Hutter, Martin Rapp
2025	J	`jnl`	Generalization Can Emerge in Tabular Foundation Models From a Single Table. CoRR Junwei Ma, Nour Shaheen, Alex Labach, Amine Mhedhbi, Frank Hutter, Anthony L. Caterini, Valentin Thomas
2025	J	`jnl`	Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics. CoRR Indrashis Das, Mahmoud Safari, Steven Adriaensen, Frank Hutter
2025	J	`jnl`	How Usable is Automated Feature Engineering for Tabular Data? CoRR Bastian Schäfer, Lennart Purucker, Maciej Janowski, Frank Hutter
2025	J	`jnl`	Improving LLM-based Global Optimization with Search Space Partitioning. CoRR Andrej Schwanke, Lyubomir Ivanov, David Salinas, Fabio Ferreira, Aaron Klein, Frank Hutter, Arber Zela
2025	J	`jnl`	Increasing LLM Coding Capabilities through Diverse Synthetic Coding Tasks. CoRR Amal Abed, Ivan Lukic, Jörg K. H. Franke, Frank Hutter
2025	A*	`conf`	KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks. ICLR Dominik Scheuer, Frederic Runge, Jörg K. H. Franke, Michael T. Wolfinger, Christoph Flamm, Frank Hutter
2025	J	`jnl`	Learning in Compact Spaces with Approximately Normalized Transformers. CoRR Jörg K. H. Franke, Urs Spiegelhalter, Marianna Nezhurina, Jenia Jitsev, Frank Hutter, Michael Hefenbrock
2025	J	`jnl`	Meta-learning Population-based Methods for Reinforcement Learning. Trans. Mach. Learn. Res. Johannes Hog, Raghu Rajan, André Biedenkapp, Noor H. Awad, Frank Hutter, Vu Nguyen
2025	A*	`conf`	Multi-objective Differentiable Neural Architecture Search. ICLR Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley, Josif Grabocka, Frank Hutter
2025	J	`jnl`	Multi-objective Hyperparameter Optimization in the Age of Deep Learning. CoRR Soham Basu, Frank Hutter, Danny Stoll
2025	—	`conf`	Position: The Future of Bayesian Prediction Is Prior-Fitted. ICML (Position Papers) Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter
2025	J	`jnl`	Position: The Future of Bayesian Prediction Is Prior-Fitted. CoRR Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter
2025	J	`jnl`	Practitioner Motives to Use Different Hyperparameter Optimization Methods. ACM Trans. Comput. Hum. Interact. Niclas Kannengießer, Niklas Hasebrook, Felix Morsbach, Marc-André Zöller, Jörg K. H. Franke, Marius Lindauer, Frank Hutter, Ali Sunyaev
2025	J	`jnl`	Quickly Tuning Foundation Models for Image Segmentation. CoRR Breenda Das, Lennart Purucker, Timur Carstensen, Frank Hutter
2025	J	`jnl`	Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data. CoRR Anurag Garg, Muhammad Ali, Noah Hollmann, Lennart Purucker, Samuel Müller, Frank Hutter
2025	—	`conf`	Regularized Neural Ensemblers. AutoML Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
2025	J	`jnl`	Selective Rotary Position Embedding. CoRR Sajad Movahedi, Timur Carstensen, Arshia Afzal, Frank Hutter, Antonio Orvieto, Volkan Cevher
2025	J	`jnl`	TabArena: A Living Benchmark for Machine Learning on Tabular Data. CoRR Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, Frank Hutter
2025	J	`jnl`	TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models. CoRR Léo Grinsztajn, Klemens Flöge, Oscar Key, Felix Birkel, Philipp Jund, Brendan Roof, Benjamin Jäger, Dominik Safaric, Simone Alessi, Adrian Hayler, Mihir Manium, Rosen Yu, Felix Jablonski, Shi Bin Hoo, Anurag Garg, Jake Robertson, Magnus Bühler, Vladyslav Moroshan, Lennart Purucker, Clara Cornu, Lilly Charlotte Wehrhahn, Alessandro Bonetto, Bernhard Schölkopf, Sauraj Gambhir, Noah Hollmann, Frank Hutter
2025	J	`jnl`	TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting. CoRR Vladyslav Moroshan, Julien Siems, Arber Zela, Timur Carstensen, Frank Hutter
2025	J	`jnl`	The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features. CoRR Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter
2025	J	`jnl`	Towards Benchmarking Foundation Models for Tabular Data With Text. CoRR Martin Mráz, Breenda Das, Anshul Gupta, Lennart Purucker, Frank Hutter
2025	J	`jnl`	Towards Scaling Laws for Symbolic Regression. CoRR David Otte, Jörg K. H. Franke, Frank Hutter
2025	J	`jnl`	Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes. CoRR Mayuka Jayawardhana, Renbo Tu, Samuel Dooley, Valeriia Cherepanova, Andrew Gordon Wilson, Frank Hutter, Colin White, Tom Goldstein, Micah Goldblum
2025	J	`jnl`	Tune My Adam, Please! CoRR Theodoros Athanasiadis, Steven Adriaensen, Samuel Müller, Frank Hutter
2025	A*	`conf`	Tuning LLM Judge Design Decisions for 1/1000 of the Cost. ICML David Salinas, Omar Swelam, Frank Hutter
2025	J	`jnl`	Tuning LLM Judges Hyperparameters. CoRR David Salinas, Omar Swelam, Frank Hutter
2025	A*	`conf`	Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. ICLR Riccardo Grazzi, Julien Siems, Arber Zela, Jörg K. H. Franke, Frank Hutter, Massimiliano Pontil
2025	J	`jnl`	Unreflected Use of Tabular Data Repositories Can Undermine Research Quality. CoRR Andrej Tschalzev, Lennart Purucker, Stefan Lüdtke, Frank Hutter, Christian Bartelt, Heiner Stuckenschmidt
2025	J	`jnl`	Where to Begin: Efficient Pretraining via Subnetwork Selection and Distillation. CoRR Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik, Gabriela Kadlecová, Vladyslav Moroshan, Timur Carstensen, Frank Hutter, Aaron Klein
2025	—	`conf`	\textttconfopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods. AutoML Abhash Kumar Jha, Shakiba Moradian, Arjun Krishnakumar, Martin Rapp, Frank Hutter
2025	J	`jnl`	carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks. CoRR Carolin Benjamins, Helena Graf, Sarah Segel, Difan Deng, Tim Ruhkopf, Leona Hennig, Soham Basu, Neeratyoy Mallik, Edward Bergman, Deyao Chen, François Clément, Matthias Feurer, Katharina Eggensperger, Frank Hutter, Carola Doerr, Marius Lindauer
2025	J	`jnl`	confopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods. CoRR Abhash Kumar Jha, Shakiba Moradian, Arjun Krishnakumar, Martin Rapp, Frank Hutter
2025	J	`jnl`	nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN. CoRR Alexander Pfefferle, Johannes Hog, Lennart Purucker, Frank Hutter
2024	A*	`conf`	A General Framework for User-Guided Bayesian Optimization. ICLR Carl Hvarfner, Frank Hutter, Luigi Nardi
2024	—	`conf`	A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes. AIES (1) Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor H. Awad
2024	J	`jnl`	A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes. CoRR Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor H. Awad
2024	J	`jnl`	AMLTK: A Modular AutoML Toolkit in Python. J. Open Source Softw. Edward Bergman, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger
2024	J	`jnl`	ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer
2024	J	`jnl`	Bayes' Power for Explaining In-Context Learning Generalizations. CoRR Samuel Müller, Noah Hollmann, Frank Hutter
2024	J	`jnl`	Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. J. Artif. Intell. Res. Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2024	—	`conf`	DAFT: Data-Aware Fine-Tuning of Foundation Models for Efficient and Effective Medical Image Segmentation. MedSAM@CVPR Alexander Pfefferle, Lennart Purucker, Frank Hutter
2024	J	`jnl`	Diffusion-based Neural Network Weights Generation. CoRR Bedionita Soro, Bruno Andreis, Hayeon Lee, Song Chong, Frank Hutter, Sung Ju Hwang
2024	—	`conf`	Don't Waste Your Time: Early Stopping Cross-Validation. AutoML Edward Bergman, Lennart Purucker, Frank Hutter
2024	J	`jnl`	Don't Waste Your Time: Early Stopping Cross-Validation. CoRR Edward Bergman, Lennart Purucker, Frank Hutter
2024	A*	`conf`	Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. NeurIPS Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter
2024	J	`jnl`	Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. CoRR Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter
2024	J	`jnl`	Dynamic Post-Hoc Neural Ensemblers. CoRR Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
2024	J	`jnl`	Efficient Search for Customized Activation Functions with Gradient Descent. CoRR Lukas Strack, Mahmoud Safari, Frank Hutter
2024	J	`jnl`	Ensembling Finetuned Language Models for Text Classification. CoRR Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
2024	J	`jnl`	FairPFN: Transformers Can do Counterfactual Fairness. CoRR Jake Robertson, Noah Hollmann, Noor H. Awad, Frank Hutter
2024	—	`conf`	Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks. AutoML Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter
2024	J	`jnl`	Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks. CoRR Shuhei Watanabe, Neeratyoy Mallik, Edward Bergman, Frank Hutter
2024	J	`jnl`	Fast Optimizer Benchmark. CoRR Simon Blauth, Tobias Bürger, Zacharias Häringer, Jörg K. H. Franke, Frank Hutter
2024	J	`jnl`	GAMformer: In-Context Learning for Generalized Additive Models. CoRR Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter
2024	—	`conf`	HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning. AutoML Gresa Shala, Sebastian Pineda-Arango, André Biedenkapp, Frank Hutter, Josif Grabocka
2024	A*	`conf`	HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models. NeurIPS Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Aaron Klein, Lennart Purucker, Jörg K. H. Franke, Frank Hutter
2024	J	`jnl`	HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models. CoRR Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Jörg K. H. Franke, Frank Hutter
2024	A*	`conf`	Improving Deep Learning Optimization through Constrained Parameter Regularization. NeurIPS Jörg K. H. Franke, Michael Hefenbrock, Gregor Köhler, Frank Hutter
2024	A*	`conf`	In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. ICML Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman, Frank Hutter
2024	J	`jnl`	In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. CoRR Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Edward Bergman, Frank Hutter
2024	—	`conf`	Is Mamba Capable of In-Context Learning? AutoML Riccardo Grazzi, Julien Niklas Siems, Simon Schrodi, Thomas Brox, Frank Hutter
2024	J	`jnl`	Is Mamba Capable of In-Context Learning? CoRR Riccardo Grazzi, Julien Siems, Simon Schrodi, Thomas Brox, Frank Hutter
2024	J	`jnl`	LLM Compression with Neural Architecture Search. CoRR Rhea Sanjay Sukthanker, Benedikt Staffler, Frank Hutter, Aaron Klein
2024	J	`jnl`	LMEMs for post-hoc analysis of HPO Benchmarking. CoRR Anton Geburek, Neeratyoy Mallik, Danny Stoll, Xavier Bouthillier, Frank Hutter
2024	J	`jnl`	Large Language Models Engineer Too Many Simple Features For Tabular Data. CoRR Jaris Küken, Lennart Purucker, Frank Hutter
2024	J	`jnl`	Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models. CoRR Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems, David Salinas, Frank Hutter
2024	J	`jnl`	Multi-objective Differentiable Neural Architecture Search. CoRR Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley, Josif Grabocka, Frank Hutter
2024	J	`jnl`	One-shot World Models Using a Transformer Trained on a Synthetic Prior. CoRR Fabio Ferreira, Moreno Schlageter, Raghu Rajan, Andre Biedenkapp, Frank Hutter
2024	J	`jnl`	Partial RNA design. Bioinform. Frederic Runge, Jörg K. H. Franke, Daniel Fertmann, Rolf Backofen, Frank Hutter
2024	A*	`conf`	Position: A Call to Action for a Human-Centered AutoML Paradigm. ICML Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	J	`jnl`	Position: A Call to Action for a Human-Centered AutoML Paradigm. CoRR Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024	A*	`conf`	Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. ICLR Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
2024	J	`jnl`	Rethinking Performance Measures of RNA Secondary Structure Problems. CoRR Frederic Runge, Jörg K. H. Franke, Daniel Fertmann, Frank Hutter
2024	A*	`conf`	Surprisingly Strong Performance Prediction with Neural Graph Features. ICML Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter
2024	J	`jnl`	Surprisingly Strong Performance Prediction with Neural Graph Features. CoRR Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter
2024	J	`jnl`	Transfer Learning for Finetuning Large Language Models. CoRR Tobias Strangmann, Lennart Purucker, Jörg K. H. Franke, Ivo Rapant, Fabio Ferreira, Frank Hutter
2024	A*	`conf`	TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. NeurIPS Benjamin Feuer, Robin Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White
2024	J	`jnl`	TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. CoRR Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White
2024	J	`jnl`	Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. CoRR Riccardo Grazzi, Julien Siems, Jörg K. H. Franke, Arber Zela, Frank Hutter, Massimiliano Pontil
2024	J	`jnl`	Warmstarting for Scaling Language Models. CoRR Neeratyoy Mallik, Maciej Janowski, Johannes Hog, Herilalaina Rakotoarison, Aaron Klein, Josif Grabocka, Frank Hutter
2024	—	`conf`	Weight-Entanglement Meets Gradient-Based Neural Architecture Search. AutoML Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter
2023	J	`jnl`	A General Framework for User-Guided Bayesian Optimization. CoRR Carl Hvarfner, Frank Hutter, Luigi Nardi
2023	J	`jnl`	Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter
2023	A*	`conf`	Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars. NeurIPS Simon Schrodi, Danny Stoll, Binxin Ru, Rhea Sanjay Sukthanker, Thomas Brox, Frank Hutter
2023	J	`jnl`	Contextualize Me - The Case for Context in Reinforcement Learning. Trans. Mach. Learn. Res. Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
2023	A*	`conf`	Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. NeurIPS Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter
2023	J	`jnl`	Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. CoRR Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter
2023	A*	`conf`	Gray-Box Gaussian Processes for Automated Reinforcement Learning. ICLR Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka
2023	J	`jnl`	Hard View Selection for Contrastive Learning. CoRR Fabio Ferreira, Ivo Rapant, Frank Hutter
2023	—	`ed.`	International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, Germany. AutoML Aleksandra Faust, Roman Garnett, Colin White, Frank Hutter, Jacob R. Gardner
2023	J	`jnl`	LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. CoRR Noah Hollmann, Samuel Müller, Frank Hutter
2023	A*	`conf`	Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. NeurIPS Noah Hollmann, Samuel Müller, Frank Hutter
2023	J	`jnl`	MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Trans. Mach. Learn. Res. Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
2023	J	`jnl`	MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning. J. Artif. Intell. Res. Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Jan Ole von Hartz, Frank Hutter
2023	J	`jnl`	MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization. CoRR Noor H. Awad, Ayushi Sharma, Philipp Muller, Janek Thomas, Frank Hutter
2023	J	`jnl`	Managing AI Risks in an Era of Rapid Progress. CoRR 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
2023	B	`conf`	Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
2023	J	`jnl`	Neural Architecture Search for Dense Prediction Tasks in Computer Vision. Int. J. Comput. Vis. Rohit Mohan, Thomas Elsken, Arber Zela, Jan Hendrik Metzen, Benedikt Staffler, Thomas Brox, Abhinav Valada, Frank Hutter
2023	J	`jnl`	Neural Architecture Search: Insights from 1000 Papers. CoRR Colin White, Mahmoud Safari, Rhea Sanjay Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, Frank Hutter
2023	J	`jnl`	New Horizons in Parameter Regularization: A Constraint Approach. CoRR Jörg K. H. Franke, Michael Hefenbrock, Gregor Köhler, Frank Hutter
2023	A*	`conf`	PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. IJCAI Shuhei Watanabe, Archit Bansal, Frank Hutter
2023	J	`jnl`	PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces. CoRR Shuhei Watanabe, Archit Bansal, Frank Hutter
2023	J	`jnl`	PFNs Are Flexible Models for Real-World Bayesian Optimization. CoRR Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter
2023	A*	`conf`	PFNs4BO: In-Context Learning for Bayesian Optimization. ICML Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter
2023	A*	`conf`	PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. NeurIPS Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
2023	J	`jnl`	PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. CoRR Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
2023	J	`jnl`	Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. CoRR Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
2023	A*	`conf`	Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition. NeurIPS Samuel Dooley, Rhea Sanjay Sukthanker, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum
2023	J	`jnl`	Scalable Deep Learning for RNA Secondary Structure Prediction. CoRR Jörg K. H. Franke, Frederic Runge, Frank Hutter
2023	A*	`conf`	Self-Correcting Bayesian Optimization through Bayesian Active Learning. NeurIPS Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi
2023	J	`jnl`	Self-Correcting Bayesian Optimization through Bayesian Active Learning. CoRR Carl Hvarfner, Erik Hellsten, Frank Hutter, Luigi Nardi
2023	A*	`conf`	Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator. IJCAI Shuhei Watanabe, Noor H. Awad, Masaki Onishi, Frank Hutter
2023	A*	`conf`	TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. ICLR Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter
2023	J	`jnl`	Towards Automated Design of Riboswitches. CoRR Frederic Runge, Jörg K. H. Franke, Frank Hutter
2023	A*	`conf`	Transfer NAS with Meta-learned Bayesian Surrogates. ICLR Gresa Shala, Thomas Elsken, Frank Hutter, Josif Grabocka
2023	J	`jnl`	Weight-Entanglement Meets Gradient-Based Neural Architecture Search. CoRR Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter
2023	A*	`conf`	c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization. IJCAI Shuhei Watanabe, Frank Hutter
2022	A*	`conf`	$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. ICLR Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
2022	J	`jnl`	Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. J. Mach. Learn. Res. Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
2022	J	`jnl`	Automated Dynamic Algorithm Configuration. CoRR Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
2022	J	`jnl`	Automated Dynamic Algorithm Configuration. J. Artif. Intell. Res. Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor H. Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
2022	J	`jnl`	Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. CoRR 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
2022	J	`jnl`	Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. J. Artif. Intell. Res. 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
2022	J	`jnl`	Contextualize Me - The Case for Context in Reinforcement Learning. CoRR Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
2022	J	`jnl`	DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. CoRR René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer
2022	—	`conf`	Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
2022	J	`jnl`	Efficient Automated Deep Learning for Time Series Forecasting. CoRR Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer
2022	—	`ed.`	International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. AutoML Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett
2022	A*	`conf`	JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search. NeurIPS Archit Bansal, Danny Stoll, Maciej Janowski, Arber Zela, Frank Hutter
2022	A*	`conf`	Joint Entropy Search For Maximally-Informed Bayesian Optimization. NeurIPS Carl Hvarfner, Frank Hutter, Luigi Nardi
2022	J	`jnl`	Joint Entropy Search For Maximally-Informed Bayesian Optimization. CoRR Carl Hvarfner, Frank Hutter, Luigi Nardi
2022	A*	`conf`	Learning Synthetic Environments and Reward Networks for Reinforcement Learning. ICLR Fabio Ferreira, Thomas Nierhoff, Andreas Sälinger, Frank Hutter
2022	J	`jnl`	Learning Synthetic Environments and Reward Networks for Reinforcement Learning. CoRR Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter
2022	J	`jnl`	Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. CoRR 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
2022	J	`jnl`	Meta-Learning a Real-Time Tabular AutoML Method For Small Data. CoRR Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter
2022	J	`jnl`	Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter
2022	J	`jnl`	Multi-objective Tree-structured Parzen Estimator Meets Meta-learning. CoRR Shuhei Watanabe, Noor H. Awad, Masaki Onishi, Frank Hutter
2022	A*	`conf`	NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. NeurIPS Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter
2022	J	`jnl`	NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies. CoRR Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter
2022	A*	`conf`	NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy. ICLR Yash Mehta, Colin White, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, Frank Hutter
2022	J	`jnl`	NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy. CoRR Yash Mehta, Colin White, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, Frank Hutter
2022	J	`jnl`	Neural Architecture Search for Dense Prediction Tasks in Computer Vision. CoRR Thomas Elsken, Arber Zela, Jan Hendrik Metzen, Benedikt Staffler, Thomas Brox, Abhinav Valada, Frank Hutter
2022	J	`jnl`	On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition. CoRR Rhea Sanjay Sukthanker, Samuel Dooley, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum
2022	J	`jnl`	On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning. CoRR Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter
2022	A*	`conf`	Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design. NeurIPS Jörg K. H. Franke, Frederic Runge, Frank Hutter
2022	J	`jnl`	Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design. CoRR Jörg K. H. Franke, Frederic Runge, Frank Hutter
2022	J	`jnl`	SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. J. Mach. Learn. Res. Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter
2022	A*	`conf`	Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks. ICLR Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, Frank Hutter
2022	A	`conf`	T3VIP: Transformation-based 3D Video Prediction. IROS Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad, Raghu Rajan, Frank Hutter, Wolfram Burgard
2022	J	`jnl`	T3VIP: Transformation-based 3D Video Prediction. CoRR Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad, Raghu Rajan, Frank Hutter, Wolfram Burgard
2022	J	`jnl`	Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration. CoRR André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr
2022	A	`conf`	Theory-inspired parameter control benchmarks for dynamic algorithm configuration. GECCO André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr
2022	J	`jnl`	Towards Discovering Neural Architectures from Scratch. CoRR Simon Schrodi, Danny Stoll, Binxin Ru, Rhea Sanjay Sukthanker, Thomas Brox, Frank Hutter
2022	A*	`conf`	Transformers Can Do Bayesian Inference. ICLR Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter
2022	J	`jnl`	Why Do Machine Learning Practitioners Still Use Manual Tuning? A Qualitative Study. CoRR Niklas Hasebrook, Felix Morsbach, Niclas Kannengießer, Jörg K. H. Franke, Frank Hutter, Ali Sunyaev
2022	J	`jnl`	Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019. CoRR 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
2022	J	`jnl`	Zero-Shot AutoML with Pretrained Models. CoRR Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter
2022	A*	`conf`	Zero-shot AutoML with Pretrained Models. ICML Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter
2022	J	`jnl`	c-TPE: Generalizing Tree-structured Parzen Estimator with Inequality Constraints for Continuous and Categorical Hyperparameter Optimization. CoRR Shuhei Watanabe, Frank Hutter
2022	J	`jnl`	πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. CoRR Carl Hvarfner, Danny Stoll, Artur L. F. Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
2021	J	`jnl`	Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Trans. Pattern Anal. Mach. Intell. Lucas Zimmer, Marius Lindauer, Frank Hutter
2021	—	`conf`	AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) 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 Sanjay Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks
2021	—	`ch.`	Automated Configuration and Selection of SAT Solvers. Handbook of Satisfiability Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2021	J	`jnl`	Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. CoRR Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Guilherme Miotto, Simon Schrodi, Andre Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter
2021	J	`jnl`	Bag of Tricks for Neural Architecture Search. CoRR Thomas Elsken, Benedikt Staffler, Arber Zela, Jan Hendrik Metzen, Frank Hutter
2021	—	`conf`	Bayesian Optimization with a Prior for the Optimum. ECML/PKDD (3) Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
2021	J	`jnl`	CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
2021	A*	`conf`	DACBench: A Benchmark Library for Dynamic Algorithm Configuration. IJCAI Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
2021	J	`jnl`	DACBench: A Benchmark Library for Dynamic Algorithm Configuration. CoRR Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
2021	A*	`conf`	DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization. IJCAI Noor H. Awad, Neeratyoy Mallik, Frank Hutter
2021	J	`jnl`	DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization. CoRR Noor H. Awad, Neeratyoy Mallik, Frank Hutter
2021	—	`conf`	HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. NeurIPS Datasets and Benchmarks Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
2021	J	`jnl`	HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. CoRR Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, Frank Hutter
2021	A*	`conf`	How Powerful are Performance Predictors in Neural Architecture Search? NeurIPS Colin White, Arber Zela, Robin Ru, Yang Liu, Frank Hutter
2021	J	`jnl`	How Powerful are Performance Predictors in Neural Architecture Search? CoRR Colin White, Arber Zela, Binxin Ru, Yang Liu, Frank Hutter
2021	J	`jnl`	In-Loop Meta-Learning with Gradient-Alignment Reward. CoRR Samuel Müller, André Biedenkapp, Frank Hutter
2021	A*	`conf`	Learning Heuristic Selection with Dynamic Algorithm Configuration. ICAPS David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
2021	J	`jnl`	Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies. CoRR Fabio Ferreira, Thomas Nierhoff, Frank Hutter
2021	—	`conf`	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) 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
2021	—	`ed.`	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part I ECML/PKDD (1) Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
2021	—	`ed.`	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part II ECML/PKDD (2) Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
2021	—	`ed.`	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part III PKDD/ECML (3) Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
2021	J	`jnl`	Multi-headed Neural Ensemble Search. CoRR Ashwin Raaghav Narayanan, Arber Zela, Tonmoy Saikia, Thomas Brox, Frank Hutter
2021	A*	`conf`	NAS-Bench-x11 and the Power of Learning Curves. NeurIPS Shen Yan, Colin White, Yash Savani, Frank Hutter
2021	J	`jnl`	NAS-Bench-x11 and the Power of Learning Curves. CoRR Shen Yan, Colin White, Yash Savani, Frank Hutter
2021	A*	`conf`	Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. NeurIPS Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh
2021	J	`jnl`	On the Importance of Domain Model Configuration for Automated Planning Engines. J. Autom. Reason. Mauro Vallati, Lukás Chrpa, Thomas Leo McCluskey, Frank Hutter
2021	A	`conf`	On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. AISTATS Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
2021	J	`jnl`	On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning. CoRR Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan O. Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
2021	—	`conf`	OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2021	J	`jnl`	OpenML-Python: an extensible Python API for OpenML. J. Mach. Learn. Res. Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2021	J	`jnl`	Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. CoRR Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
2021	J	`jnl`	SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter
2021	A*	`conf`	Sample-Efficient Automated Deep Reinforcement Learning. ICLR Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter
2021	A*	`conf`	Self-Paced Context Evaluation for Contextual Reinforcement Learning. ICML Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
2021	J	`jnl`	Self-Paced Context Evaluation for Contextual Reinforcement Learning. CoRR Theresa Eimer, André Biedenkapp, Frank Hutter, Marius Lindauer
2021	B	`conf`	Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. IJCNN Jovita Lukasik, David Friede, Arber Zela, Frank Hutter, Margret Keuper
2021	A*	`conf`	TempoRL: Learning When to Act. ICML André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
2021	J	`jnl`	TempoRL: Learning When to Act. CoRR André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
2021	J	`jnl`	Transformers Can Do Bayesian Inference. CoRR Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter
2021	A*	`conf`	TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. ICCV Samuel G. Müller, Frank Hutter
2021	J	`jnl`	TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. CoRR Samuel G. Müller, Frank Hutter
2021	A*	`conf`	Well-tuned Simple Nets Excel on Tabular Datasets. NeurIPS Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
2021	J	`jnl`	Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Trans. Pattern Anal. Mach. Intell. 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
2020	J	`jnl`	Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. CoRR Lucas Zimmer, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Auto-Sklearn 2.0: The Next Generation. CoRR Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Convergence Analysis of Homotopy-SGD for non-convex optimization. CoRR Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter
2020	J	`jnl`	Differential Evolution for Neural Architecture Search. CoRR Noor H. Awad, Neeratyoy Mallik, Frank Hutter
2020	A	`conf`	Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. ECAI André Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer
2020	J	`jnl`	Hyperparameter Transfer Across Developer Adjustments. CoRR Danny Stoll, Jörg K. H. Franke, Diane Wagner, Simon Selg, Frank Hutter
2020	J	`jnl`	Learning Heuristic Selection with Dynamic Algorithm Configuration. CoRR David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
2020	—	`conf`	Learning Step-Size Adaptation in CMA-ES. PPSN (1) Gresa Shala, André Biedenkapp, Noor H. Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Machine-Learning-Based Diagnostics of EEG Pathology. CoRR Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabaszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball
2020	J	`jnl`	Machine-learning-based diagnostics of EEG pathology. NeuroImage Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabaszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball
2020	A*	`conf`	Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization. ICLR Michael Volpp, Lukas P. Fröhlich, Kirsten Fischer, Andreas Doerr, Stefan Falkner, Frank Hutter, Christian Daniel
2020	A*	`conf`	Meta-Learning of Neural Architectures for Few-Shot Learning. CVPR Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter
2020	A*	`conf`	NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search. ICLR Arber Zela, Julien Siems, Frank Hutter
2020	J	`jnl`	NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search. CoRR Arber Zela, Julien Siems, Frank Hutter
2020	J	`jnl`	NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search. CoRR Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, Frank Hutter
2020	J	`jnl`	Neural Ensemble Search for Performant and Calibrated Predictions. CoRR Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh
2020	J	`jnl`	Neural Model-based Optimization with Right-Censored Observations. CoRR Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter
2020	J	`jnl`	On the Importance of Domain Model Configuration for Automated Planning Engines. CoRR Mauro Vallati, Lukás Chrpa, Thomas Leo McCluskey, Frank Hutter
2020	J	`jnl`	On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs. CoRR Matilde Gargiani, Andrea Zanelli, Moritz Diehl, Frank Hutter
2020	J	`jnl`	Prior-guided Bayesian Optimization. CoRR Artur L. F. Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
2020	J	`jnl`	Sample-Efficient Automated Deep Reinforcement Learning. CoRR Jörg K. H. Franke, Gregor Köhler, André Biedenkapp, Frank Hutter
2020	J	`jnl`	Smooth Variational Graph Embeddings for Efficient Neural Architecture Search. CoRR Jovita Lukasik, David Friede, Arber Zela, Heiner Stuckenschmidt, Frank Hutter, Margret Keuper
2020	J	`jnl`	Squirrel: A Switching Hyperparameter Optimizer. CoRR 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
2020	J	`jnl`	The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. Artif. Life 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
2020	J	`jnl`	The locality dilemma of Sankoff-like RNA alignments. Bioinform. Teresa Müller, Milad Miladi, Frank Hutter, Ivo L. Hofacker, Sebastian Will, Rolf Backofen
2020	A*	`conf`	Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl, Frank Hutter
2020	A*	`conf`	Understanding and Robustifying Differentiable Architecture Search. ICLR Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter
2019	J	`jnl`	!MDP Playground: Meta-Features in Reinforcement Learning. CoRR Raghu Rajan, Frank Hutter
2019	A*	`conf`	An Evolution Strategy with Progressive Episode Lengths for Playing Games. IJCAI Lior Fuks, Noor H. Awad, Frank Hutter, Marius Lindauer
2019	—	`ch.`	Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. Automated Machine Learning Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2019	—	`ch.`	Auto-sklearn: Efficient and Robust Automated Machine Learning. Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter
2019	A*	`conf`	AutoDispNet: Improving Disparity Estimation With AutoML. ICCV Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, Thomas Brox
2019	J	`jnl`	AutoDispNet: Improving Disparity Estimation with AutoML. CoRR Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, Thomas Brox
2019	—	`book`	Automated Machine Learning - Methods, Systems, Challenges Automated Machine Learning Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
2019	J	`jnl`	BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. CoRR Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Joshua Marben, Philipp Müller, Frank Hutter
2019	J	`jnl`	Best Practices for Scientific Research on Neural Architecture Search. CoRR Marius Lindauer, Frank Hutter
2019	—	`conf`	Decoupled Weight Decay Regularization. ICLR (Poster) Ilya Loshchilov, Frank Hutter
2019	—	`conf`	Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR (Poster) Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
2019	B	`conf`	Hyperparameter Importance for Image Classification by Residual Neural Networks. DS Abhinav Sharma, Jan N. van Rijn, Frank Hutter, Andreas Müller
2019	—	`ch.`	Hyperparameter Optimization. Automated Machine Learning Matthias Feurer, Frank Hutter
2019	—	`conf`	Learning to Design RNA. ICLR (Poster) Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter
2019	J	`jnl`	Meta-Learning Acquisition Functions for Bayesian Optimization. CoRR Michael Volpp, Lukas P. Fröhlich, Andreas Doerr, Frank Hutter, Christian Daniel
2019	J	`jnl`	Meta-Learning of Neural Architectures for Few-Shot Learning. CoRR Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter
2019	A*	`conf`	Meta-Surrogate Benchmarking for Hyperparameter Optimization. NeurIPS Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González
2019	J	`jnl`	Meta-Surrogate Benchmarking for Hyperparameter Optimization. CoRR Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González
2019	A*	`conf`	NAS-Bench-101: Towards Reproducible Neural Architecture Search. ICML Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter
2019	J	`jnl`	NAS-Bench-101: Towards Reproducible Neural Architecture Search. CoRR Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter
2019	J	`jnl`	Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control. CoRR Jörg K. H. Franke, Gregor Köhler, Noor H. Awad, Frank Hutter
2019	—	`ch.`	Neural Architecture Search. Automated Machine Learning Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
2019	J	`jnl`	Neural Architecture Search: A Survey. J. Mach. Learn. Res. Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
2019	J	`jnl`	OpenML-Python: an extensible Python API for OpenML. CoRR Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2019	—	`conf`	Optimizing Neural Networks for Patent Classification. ECML/PKDD (3) Louay Abdelgawad, Peter Kluegl, Erdan Genc, Stefan Falkner, Frank Hutter
2019	J	`jnl`	Pitfalls and Best Practices in Algorithm Configuration. J. Artif. Intell. Res. Katharina Eggensperger, Marius Lindauer, Frank Hutter
2019	J	`jnl`	Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings. CoRR Matilde Gargiani, Aaron Klein, Stefan Falkner, Frank Hutter
2019	J	`jnl`	Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization. CoRR Aaron Klein, Frank Hutter
2019	J	`jnl`	Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters. CoRR Marius Lindauer, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Frank Hutter
2019	—	`ch.`	Towards Automatically-Tuned Deep Neural Networks. Automated Machine Learning Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter
2019	J	`jnl`	Towards White-box Benchmarks for Algorithm Control. CoRR André Biedenkapp, H. Furkan Bozkurt, Frank Hutter, Marius Lindauer
2019	J	`jnl`	Understanding and Robustifying Differentiable Architecture Search. CoRR Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, Frank Hutter
2018	J	`jnl`	A case study of algorithm selection for the traveling thief problem. J. Heuristics Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter
2018	A*	`conf`	BOHB: Robust and Efficient Hyperparameter Optimization at Scale. ICML Stefan Falkner, Aaron Klein, Frank Hutter
2018	J	`jnl`	BOHB: Robust and Efficient Hyperparameter Optimization at Scale. CoRR Stefan Falkner, Aaron Klein, Frank Hutter
2018	A*	`conf`	Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari. IJCAI Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter
2018	J	`jnl`	Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari. CoRR Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter
2018	—	`conf`	CAVE: Configuration Assessment, Visualization and Evaluation. LION Andre Biedenkapp, Joshua Marben, Marius Lindauer, Frank Hutter
2018	B	`conf`	Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML. IDA Benjamin Strang, Peter van der Putten, Jan N. van Rijn, Frank Hutter
2018	J	`jnl`	Efficient benchmarking of algorithm configurators via model-based surrogates. Mach. Learn. Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2018	J	`jnl`	Generative Reversible Networks. CoRR Robin Tibor Schirrmeister, Patryk Chrabaszcz, Frank Hutter, Tonio Ball
2018	A*	`conf`	Hyperparameter Importance Across Datasets. KDD Jan N. van Rijn, Frank Hutter
2018	J	`jnl`	Learning to Design RNA. CoRR Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter
2018	A*	`conf`	Maximizing acquisition functions for Bayesian optimization. NeurIPS James T. Wilson, Frank Hutter, Marc Peter Deisenroth
2018	J	`jnl`	Maximizing acquisition functions for Bayesian optimization. CoRR James T. Wilson, Frank Hutter, Marc Peter Deisenroth
2018	J	`jnl`	Multi-objective Architecture Search for CNNs. CoRR Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
2018	J	`jnl`	Neural Architecture Search: A Survey. CoRR Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
2018	A*	`conf`	Neural Networks for Predicting Algorithm Runtime Distributions. IJCAI Katharina Eggensperger, Marius Lindauer, Frank Hutter
2018	—	`conf`	Practical Hyperparameter Optimization for Deep Learning. ICLR (Workshop) Stefan Falkner, Aaron Klein, Frank Hutter
2018	—	`ch.`	Selection and Configuration of Parallel Portfolios. Handbook of Parallel Constraint Reasoning Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2018	—	`conf`	Simple and efficient architecture search for Convolutional Neural Networks. ICLR (Workshop) Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
2018	—	`conf`	Summary of evolutionary computation for wind farm layout optimization. GECCO (Companion) Dennis G. Wilson, Silvio Rodrigues, Carlos Segura, Ilya Loshchilov, Frank Hutter, Guillermo López Buenfil, Ahmed Kheiri, Ed Keedwell, Mario Ocampo-Pineda, Ender Özcan, Sergio Ivvan Valdez Peña, Brian Goldman, Salvador Botello Rionda, Arturo Hernández Aguirre, Kalyan Veeramachaneni, Sylvain Cussat-Blanc
2018	J	`jnl`	The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. CoRR 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
2018	J	`jnl`	Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search. CoRR Arber Zela, Aaron Klein, Stefan Falkner, Frank Hutter
2018	—	`conf`	Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow. ECCV (7) Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018	J	`jnl`	Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. CoRR Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018	A*	`conf`	Warmstarting of Model-Based Algorithm Configuration. AAAI Marius Lindauer, Frank Hutter
2017	J	`jnl`	A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets. CoRR Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter
2017	—	`conf`	An Empirical Study of Hyperparameter Importance Across Datasets. AutoML@PKDD/ECML Jan N. van Rijn, Frank Hutter
2017	J	`jnl`	Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2017	A*	`conf`	AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). IJCAI Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub
2017	J	`jnl`	Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG. CoRR Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball
2017	J	`jnl`	Deep learning with convolutional neural networks for decoding and visualization of EEG pathology. CoRR Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank Hutter, Tonio Ball
2017	J	`jnl`	Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates. CoRR Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
2017	A*	`conf`	Efficient Parameter Importance Analysis via Ablation with Surrogates. AAAI Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger H. Hoos
2017	A	`conf`	Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. AISTATS Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter
2017	J	`jnl`	Fixing Weight Decay Regularization in Adam. CoRR Ilya Loshchilov, Frank Hutter
2017	J	`jnl`	Hyperparameter Importance Across Datasets. CoRR Jan N. van Rijn, Frank Hutter
2017	—	`conf`	Learning Curve Prediction with Bayesian Neural Networks. ICLR (Poster) Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter
2017	—	`conf`	OASC-2017: *Zilla Submission. OASC Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown, Frank Hutter
2017	J	`jnl`	OpenML Benchmarking Suites and the OpenML100. CoRR Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2017	J	`jnl`	Pitfalls and Best Practices in Algorithm Configuration. CoRR Katharina Eggensperger, Marius Lindauer, Frank Hutter
2017	J	`jnl`	Predicting Runtime Distributions using Deep Neural Networks. CoRR Katharina Eggensperger, Marius Lindauer, Frank Hutter
2017	—	`ed.`	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. AutoML@PKDD/ECML Pavel Brazdil, Joaquin Vanschoren, Frank Hutter, Holger H. Hoos
2017	—	`conf`	SGDR: Stochastic Gradient Descent with Warm Restarts. ICLR (Poster) Ilya Loshchilov, Frank Hutter
2017	J	`jnl`	Simple And Efficient Architecture Search for Convolutional Neural Networks. CoRR Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
2017	J	`jnl`	The Configurable SAT Solver Challenge (CSSC). Artif. Intell. Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown
2017	—	`conf`	The Sacred Infrastructure for Computational Research. SciPy Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, Jürgen Schmidhuber
2017	J	`jnl`	The reparameterization trick for acquisition functions. CoRR James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc Peter Deisenroth
2017	J	`jnl`	Warmstarting of Model-based Algorithm Configuration. CoRR Marius Lindauer, Frank Hutter
2016	J	`jnl`	A case study of algorithm selection for the traveling thief problem. CoRR Markus Wagner, Marius Lindauer, Mustafa Misir, Samadhi Nallaperuma, Frank Hutter
2016	J	`jnl`	ASlib: A benchmark library for algorithm selection. Artif. Intell. Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
2016	—	`conf`	An Empirical Study of Per-instance Algorithm Scheduling. LION Marius Lindauer, Rolf-David Bergdoll, Frank Hutter
2016	J	`jnl`	Asynchronous Stochastic Gradient MCMC with Elastic Coupling. CoRR Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter
2016	A*	`conf`	Automatic bone parameter estimation for skeleton tracking in optical motion capture. ICRA Tobias Schubert, Katharina Eggensperger, Alexis Gkogkidis, Frank Hutter, Tonio Ball, Wolfram Burgard
2016	J	`jnl`	Bayesian Optimization in a Billion Dimensions via Random Embeddings. J. Artif. Intell. Res. Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, Nando de Freitas
2016	—	`conf`	Bayesian Optimization with Robust Bayesian Neural Networks. NIPS Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter
2016	J	`jnl`	CMA-ES for Hyperparameter Optimization of Deep Neural Networks. CoRR Ilya Loshchilov, Frank Hutter
2016	J	`jnl`	Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. CoRR Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter
2016	—	`ed.`	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 AutoML@ICML Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
2016	J	`jnl`	SGDR: Stochastic Gradient Descent with Restarts. CoRR Ilya Loshchilov, Frank Hutter
2016	—	`conf`	Towards Automatically-Tuned Neural Networks. AutoML@ICML Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
2015	J	`jnl`	ASlib: A Benchmark Library for Algorithm Selection. CoRR Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren
2015	—	`ed.`	Algorithm Configuration, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015. AAAI Workshop: Algorithm Configuration Frank Hutter, Marius Lindauer, Yuri Malitsky
2015	A*	`conf`	Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract). IJCAI Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown
2015	—	`conf`	AutoFolio: Algorithm Configuration for Algorithm Selection. AAAI Workshop: Algorithm Configuration Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub
2015	J	`jnl`	AutoFolio: An Automatically Configured Algorithm Selector. J. Artif. Intell. Res. Marius Lindauer, Holger H. Hoos, Frank Hutter, Torsten Schaub
2015	A*	`conf`	Automatic Configuration of Sequential Planning Portfolios. AAAI Jendrik Seipp, Silvan Sievers, Malte Helmert, Frank Hutter
2015	J	`jnl`	Beyond Manual Tuning of Hyperparameters. Künstliche Intell. Frank Hutter, Jörg Lücke, Lars Schmidt-Thieme
2015	A*	`conf`	Efficient Benchmarking of Hyperparameter Optimizers via Surrogates. AAAI Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2015	—	`conf`	Efficient and Robust Automated Machine Learning. NIPS Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter
2015	—	`conf`	From Sequential Algorithm Selection to Parallel Portfolio Selection. LION Marius Lindauer, Holger H. Hoos, Frank Hutter
2015	A*	`conf`	Initializing Bayesian Hyperparameter Optimization via Meta-Learning. AAAI Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
2015	A*	`conf`	On the Effective Configuration of Planning Domain Models. IJCAI Mauro Vallati, Frank Hutter, Lukás Chrpa, Thomas Leo McCluskey
2015	J	`jnl`	Online Batch Selection for Faster Training of Neural Networks. CoRR Ilya Loshchilov, Frank Hutter
2015	A*	`conf`	Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves. IJCAI Tobias Domhan, Jost Tobias Springenberg, Frank Hutter
2015	A	`conf`	SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. SAT Stefan Falkner, Marius Lindauer, Frank Hutter
2015	J	`jnl`	The Configurable SAT Solver Challenge (CSSC). CoRR Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown
2014	—	`conf`	AClib: A Benchmark Library for Algorithm Configuration. LION Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle
2014	—	`conf`	Algorithm Configuration in the Cloud: A Feasibility Study. LION Daniel Geschwender, Frank Hutter, Lars Kotthoff, Yuri Malitsky, Holger H. Hoos, Kevin Leyton-Brown
2014	J	`jnl`	Algorithm runtime prediction: Methods & evaluation. Artif. Intell. Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown
2014	A*	`conf`	An Efficient Approach for Assessing Hyperparameter Importance. ICML Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2014	—	`conf`	Bayesian Optimization for More Automatic Machine Learning. MetaSel@ECAI Frank Hutter
2014	A*	`conf`	Improved Features for Runtime Prediction of Domain-Independent Planners. ICAPS Chris Fawcett, Mauro Vallati, Frank Hutter, Jörg Hoffmann, Holger H. Hoos, Kevin Leyton-Brown
2014	J	`jnl`	ParamILS: An Automatic Algorithm Configuration Framework. CoRR Frank Hutter, Thomas Stützle, Kevin Leyton-Brown, Holger H. Hoos
2014	—	`conf`	Surrogate Benchmarks for Hyperparameter Optimization. MetaSel@ECAI Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2014	J	`jnl`	Understanding the empirical hardness of Commun. ACM Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, Lin Xu
2014	—	`conf`	Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters. MetaSel@ECAI Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
2013	J	`jnl`	A Kernel for Hierarchical Parameter Spaces. CoRR Frank Hutter, Michael A. Osborne
2013	—	`conf`	An evaluation of sequential model-based optimization for expensive blackbox functions. GECCO (Companion) Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2013	A*	`conf`	Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. KDD Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2013	J	`jnl`	Bayesian Optimization With Censored Response Data. CoRR Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2013	A*	`conf`	Bayesian Optimization in High Dimensions via Random Embeddings. IJCAI Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas
2013	J	`jnl`	Bayesian Optimization in a Billion Dimensions via Random Embeddings CoRR Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas
2013	—	`conf`	Identifying Key Algorithm Parameters and Instance Features Using Forward Selection. LION Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2012	J	`jnl`	Algorithm Runtime Prediction: The State of the Art CoRR Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown
2012	J	`jnl`	Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms CoRR Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2012	A	`conf`	Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors. SAT Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2012	—	`conf`	Parallel Algorithm Configuration. LION Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2011	J	`jnl`	SATzilla: Portfolio-based Algorithm Selection for SAT CoRR Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2011	—	`conf`	Sequential Model-Based Optimization for General Algorithm Configuration. LION Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2010	B	`conf`	Automated Configuration of Mixed Integer Programming Solvers. CPAIOR Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2010	—	`ch.`	Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches. Experimental Methods for the Analysis of Optimization Algorithms Frank Hutter, Thomas Bartz-Beielstein, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy
2010	—	`conf`	Time-Bounded Sequential Parameter Optimization. LION Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy
2010	J	`jnl`	Tradeoffs in the empirical evaluation of competing algorithm designs. Ann. Math. Artif. Intell. Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2009	A	`conf`	An experimental investigation of model-based parameter optimisation: SPO and beyond. GECCO Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy
2009	J	`jnl`	ParamILS: An Automatic Algorithm Configuration Framework. J. Artif. Intell. Res. Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle
2008	J	`jnl`	SATzilla: Portfolio-based Algorithm Selection for SAT. J. Artif. Intell. Res. Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2007	A	`conf`	: The Design and Analysis of an Algorithm Portfolio for SAT. CP Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
2007	A*	`conf`	Automatic Algorithm Configuration Based on Local Search. AAAI Frank Hutter, Holger H. Hoos, Thomas Stützle
2007	B	`conf`	Boosting Verification by Automatic Tuning of Decision Procedures. FMCAD Frank Hutter, Domagoj Babic, Holger H. Hoos, Alan J. Hu
2006	A	`conf`	Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. CP Frank Hutter, Youssef Hamadi, Holger H. Hoos, Kevin Leyton-Brown
2006	J	`jnl`	Reports on the Twenty-First National Conference on Artificial Intelligence (AAAI-06) Workshop Program. AI Mag. 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
2005	A*	`conf`	Efficient Stochastic Local Search for MPE Solving. IJCAI Frank Hutter, Holger H. Hoos, Thomas Stützle
2004	J	`jnl`	Diagnosis by a waiter and a Mars explorer. Proc. IEEE Nando de Freitas, Richard Dearden, Frank Hutter, Rubén Morales-Menéndez, Jim Mutch, David Poole
2002	A	`conf`	Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT. CP Frank Hutter, Dave A. D. Tompkins, Holger H. Hoos