Frank Hutter
106 publications
24 venues
H Index 34
Affiliation
University of Freiburg, GermanyLinks
| Name | Venue | Year | citations |
|---|---|---|---|
Position: A Call to Action for a Human-Centered AutoML Paradigm.
|
ICML | 2024 | 0 |
| In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization.
|
ICML | 2024 | 0 |
| Surprisingly Strong Performance Prediction with Neural Graph Features.
|
ICML | 2024 | 0 |
| Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How.
|
ICLR | 2024 | 0 |
| A General Framework for User-Guided Bayesian Optimization.
|
ICLR | 2024 | 0 |
| Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML.
|
JAIR | 2024 | 0 |
| PFNs4BO: In-Context Learning for Bayesian Optimization.
|
ICML | 2023 | 0 |
| Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator.
|
IJCAI | 2023 | 0 |
| PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces.
|
IJCAI | 2023 | 0 |
| c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization.
|
IJCAI | 2023 | 0 |
| Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks.
|
NIPS/NeurIPS | 2023 | 0 |
| Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars.
|
NIPS/NeurIPS | 2023 | 0 |
| Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering.
|
NIPS/NeurIPS | 2023 | 0 |
| Self-Correcting Bayesian Optimization through Bayesian Active Learning.
|
NIPS/NeurIPS | 2023 | 0 |
| Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition.
|
NIPS/NeurIPS | 2023 | 0 |
| PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning.
|
NIPS/NeurIPS | 2023 | 0 |
| Gray-Box Gaussian Processes for Automated Reinforcement Learning.
|
ICLR | 2023 | 0 |
| Transfer NAS with Meta-learned Bayesian Surrogates.
|
ICLR | 2023 | 0 |
| TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.
|
ICLR | 2023 | 0 |
| MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning.
|
JAIR | 2023 | 0 |
| Zero-shot AutoML with Pretrained Models.
|
ICML | 2022 | 0 |
| Learning Synthetic Environments and Reward Networks for Reinforcement Learning.
|
ICLR | 2022 | 0 |
| Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.
|
JAIR | 2022 | 28 |
| NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy.
|
ICLR | 2022 | 10 |
| Automated Dynamic Algorithm Configuration.
|
JAIR | 2022 | 2 |
| Efficient Automated Deep Learning for Time Series Forecasting.
|
ECML/PKDD | 2022 | 0 |
| Joint Entropy Search For Maximally-Informed Bayesian Optimization.
|
NIPS/NeurIPS | 2022 | 0 |
| NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies.
|
NIPS/NeurIPS | 2022 | 0 |
| JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search.
|
NIPS/NeurIPS | 2022 | 0 |
| Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design.
|
NIPS/NeurIPS | 2022 | 0 |
| Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks.
|
ICLR | 2022 | 0 |
| Transformers Can Do Bayesian Inference.
|
ICLR | 2022 | 0 |
| $\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.
|
ICLR | 2022 | 0 |
| T3VIP: Transformation-based 3D Video Prediction.
|
IROS | 2022 | 0 |
| SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
|
JMLR | 2022 | 0 |
| Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning.
|
JMLR | 2022 | 0 |
| DACBench: A Benchmark Library for Dynamic Algorithm Configuration.
|
IJCAI | 2021 | 12 |
| Well-tuned Simple Nets Excel on Tabular Datasets.
|
NIPS/NeurIPS | 2021 | 43 |
| NAS-Bench-x11 and the Power of Learning Curves.
|
NIPS/NeurIPS | 2021 | 10 |
| Self-Paced Context Evaluation for Contextual Reinforcement Learning.
|
ICML | 2021 | 7 |
| DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization.
|
IJCAI | 2021 | 34 |
| Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
|
TPAMI | 2021 | 30 |
| How Powerful are Performance Predictors in Neural Architecture Search?
|
NIPS/NeurIPS | 2021 | 50 |
| Bayesian Optimization with a Prior for the Optimum.
|
ECML/PKDD | 2021 | 17 |
| On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning.
|
AISTATS | 2021 | 51 |
| TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation.
|
ICCV | 2021 | 46 |
| TempoRL: Learning When to Act.
|
ICML | 2021 | 8 |
| Smooth Variational Graph Embeddings for Efficient Neural Architecture Search.
|
IJCNN | 2021 | 0 |
| Learning Heuristic Selection with Dynamic Algorithm Configuration.
|
ICAPS | 2021 | 0 |
| Neural Ensemble Search for Uncertainty Estimation and Dataset Shift.
|
NIPS/NeurIPS | 2021 | 0 |
| Sample-Efficient Automated Deep Reinforcement Learning.
|
ICLR | 2021 | 0 |
| Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019.
|
TPAMI | 2021 | 0 |
| OpenML-Python: an extensible Python API for OpenML.
|
JMLR | 2021 | 0 |
| Transferring Optimality Across Data Distributions via Homotopy Methods.
|
ICLR | 2020 | 2 |
| NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search.
|
ICLR | 2020 | 116 |
| Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework.
|
ECAI | 2020 | 36 |
| Meta-Learning of Neural Architectures for Few-Shot Learning.
|
CVPR | 2020 | 0 |
| Understanding and Robustifying Differentiable Architecture Search.
|
ICLR | 2020 | 0 |
| Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization.
|
ICLR | 2020 | 0 |
| AutoDispNet: Improving Disparity Estimation With AutoML.
|
ICCV | 2019 | 52 |
| Meta-Surrogate Benchmarking for Hyperparameter Optimization.
|
NIPS/NeurIPS | 2019 | 32 |
| NAS-Bench-101: Towards Reproducible Neural Architecture Search.
|
ICML | 2019 | 384 |
| An Evolution Strategy with Progressive Episode Lengths for Playing Games.
|
IJCAI | 2019 | 6 |
| Optimizing Neural Networks for Patent Classification.
|
ECML/PKDD | 2019 | 13 |
| Pitfalls and Best Practices in Algorithm Configuration.
|
JAIR | 2019 | 0 |
| Neural Architecture Search: A Survey.
|
JMLR | 2019 | 0 |
| BOHB: Robust and Efficient Hyperparameter Optimization at Scale.
|
ICML | 2018 | 682 |
| Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari.
|
IJCAI | 2018 | 73 |
| Maximizing acquisition functions for Bayesian optimization.
|
NIPS/NeurIPS | 2018 | 147 |
| Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow.
|
ECCV | 2018 | 144 |
| Warmstarting of Model-Based Algorithm Configuration.
|
AAAI | 2018 | 0 |
| Neural Networks for Predicting Algorithm Runtime Distributions.
|
IJCAI | 2018 | 0 |
| Hyperparameter Importance Across Datasets.
|
KDD | 2018 | 0 |
| Efficient benchmarking of algorithm configurators via model-based surrogates.
|
MLJ | 2018 | 0 |
| Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA.
|
JMLR | 2017 | 575 |
| AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract).
|
IJCAI | 2017 | 8 |
| Efficient Parameter Importance Analysis via Ablation with Surrogates.
|
AAAI | 2017 | 31 |
| Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets.
|
AISTATS | 2017 | 0 |
| The Configurable SAT Solver Challenge (CSSC).
|
Artificial Intelligence | 2017 | 0 |
| Bayesian Optimization with Robust Bayesian Neural Networks.
|
NIPS/NeurIPS | 2016 | 343 |
| Automatic bone parameter estimation for skeleton tracking in optical motion capture.
|
ICRA | 2016 | 14 |
| Bayesian Optimization in a Billion Dimensions via Random Embeddings.
|
JAIR | 2016 | 0 |
| ASlib: A benchmark library for algorithm selection.
|
Artificial Intelligence | 2016 | 0 |
| Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract).
|
IJCAI | 2015 | 60 |
| On the Effective Configuration of Planning Domain Models.
|
IJCAI | 2015 | 39 |
| Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves.
|
IJCAI | 2015 | 494 |
| Efficient and Robust Automated Machine Learning.
|
NIPS/NeurIPS | 2015 | 1313 |
| Efficient Benchmarking of Hyperparameter Optimizers via Surrogates.
|
AAAI | 2015 | 106 |
| SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers.
|
SAT | 2015 | 19 |
| Initializing Bayesian Hyperparameter Optimization via Meta-Learning.
|
AAAI | 2015 | 364 |
| AutoFolio: An Automatically Configured Algorithm Selector.
|
JAIR | 2015 | 115 |
| Automatic Configuration of Sequential Planning Portfolios.
|
AAAI | 2015 | 53 |
| Improved Features for Runtime Prediction of Domain-Independent Planners.
|
ICAPS | 2014 | 51 |
| An Efficient Approach for Assessing Hyperparameter Importance.
|
ICML | 2014 | 329 |
| Algorithm runtime prediction: Methods & evaluation.
|
Artificial Intelligence | 2014 | 0 |
| Bayesian Optimization in High Dimensions via Random Embeddings.
|
IJCAI | 2013 | 302 |
| Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms.
|
KDD | 2013 | 0 |
| Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors.
|
SAT | 2012 | 103 |
| Automated Configuration of Mixed Integer Programming Solvers.
|
CPAIOR | 2010 | 186 |
| ParamILS: An Automatic Algorithm Configuration Framework.
|
JAIR | 2009 | 992 |
| SATzilla: Portfolio-based Algorithm Selection for SAT.
|
JAIR | 2008 | 915 |
| Automatic Algorithm Configuration Based on Local Search.
|
AAAI | 2007 | 325 |
| : The Design and Analysis of an Algorithm Portfolio for SAT.
|
CP | 2007 | 142 |
| Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms.
|
CP | 2006 | 202 |
| Efficient Stochastic Local Search for MPE Solving.
|
IJCAI | 2005 | 51 |
| Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT.
|
CP | 2002 | 240 |