← Authors

Frank Hutter

94 papers · 2013–2025 · 10 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (10) πŸƒ Academic Marathon (12) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (9) πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏠 Conference Loyalist (22) πŸ† Keyword Champion (8) πŸ”¬ Deep Specialist (10) 🌱 Topic Pioneer πŸ‘‘ Triple Crown 🀝 Dynamic Duo (14) πŸ“ˆ Trend Setter ❓ The Questioner (2) ⚑ Prolific Year (14) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (57) πŸ’Ž Century Club (94) πŸ”₯ Unstoppable (13)

Conferences

ICLR (23) NIPS (22) ICML (14) IJCAI (13) AUTOML (9) JMLR (7) AISTATS (2) ICCV (2) CVPR (1) ECCV (1)

Top co-authors

Marius Lindauer (14) Arber Zela (13) Josif Grabocka (9) Aaron Klein (9) Samuel MΓΌller (8) AndrΓ© Biedenkapp (8) Matthias Feurer (7) Noah Hollmann (7) JΓΆrg K.H. Franke (6) Thomas Elsken (6)

Keywords

hyperparameter optimization (25) bayesian optimization (15) neural architecture search (13) automated machine learning (7) model selection (6) surrogate model (6) prior-data fitted network (4) reinforcement learning (3) multi-objective optimization (3) acquisition function (3) gaussian process (3) neural network (3) tabular datum (3) in-context learning (3) deep learning (3) atari game (2) bayesian inference (2) neural network optimization (2) game playing (2) sample efficiency (2)

Papers

Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization AUTOML 2025
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks ICML 2025
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues ICLR 2025
KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks ICLR 2025
Diffusion-based Neural Network Weights Generation ICLR 2025
Position: The Future of Bayesian Prediction Is Prior-Fitted ICML 2025
Tuning LLM Judge Design Decisions for 1/1000 of the Cost ICML 2025
FairPFN: A Tabular Foundation Model for Causal Fairness ICML 2025
DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning JMLR 2025
Multi-objective Differentiable Neural Architecture Search ICLR 2025
Beyond Random Augmentations: Pretraining with Hard Views ICLR 2025
\textttconfopt: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods AUTOML 2025
Regularized Neural Ensemblers AUTOML 2025
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks NIPS 2024
A General Framework for User-Guided Bayesian Optimization ICLR 2024
Improving Deep Learning Optimization through Constrained Parameter Regularization NIPS 2024
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models NIPS 2024
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How ICLR 2024
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data NIPS 2024
Is Mamba Capable of In-Context Learning? AUTOML 2024
Don’t Waste Your Time: Early Stopping Cross-Validation AUTOML 2024
Weight-Entanglement Meets Gradient-Based Neural Architecture Search AUTOML 2024
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks AUTOML 2024
HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning AUTOML 2024
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization ICML 2024
Position: A Call to Action for a Human-Centered AutoML Paradigm ICML 2024
Surprisingly Strong Performance Prediction with Neural Graph Features ICML 2024
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces IJCAI 2023
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering NIPS 2023
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition NIPS 2023
Self-Correcting Bayesian Optimization through Bayesian Active Learning NIPS 2023
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks NIPS 2023
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning NIPS 2023
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization IJCAI 2023
Gray-Box Gaussian Processes for Automated Reinforcement Learning ICLR 2023
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars NIPS 2023
Transfer NAS with Meta-learned Bayesian Surrogates ICLR 2023
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second ICLR 2023
PFNs4BO: In-Context Learning for Bayesian Optimization ICML 2023
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator IJCAI 2023
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy ICLR 2022
Joint Entropy Search For Maximally-Informed Bayesian Optimization NIPS 2022
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design NIPS 2022
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies NIPS 2022
JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search NIPS 2022
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization ICLR 2022
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks ICLR 2022
Transformers Can Do Bayesian Inference ICLR 2022
Learning Synthetic Environments and Reward Networks for Reinforcement Learning ICLR 2022
Zero-shot AutoML with Pretrained Models ICML 2022
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization JMLR 2022
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning JMLR 2022
Self-Paced Context Evaluation for Contextual Reinforcement Learning ICML 2021
How Powerful are Performance Predictors in Neural Architecture Search? NIPS 2021
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning AISTATS 2021
Sample-Efficient Automated Deep Reinforcement Learning ICLR 2021
OpenML-Python: an extensible Python API for OpenML JMLR 2021
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift NIPS 2021
TrivialAugment: Tuning-Free Yet State-of-the-Art Data Augmentation ICCV 2021
DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization IJCAI 2021
DACBench: A Benchmark Library for Dynamic Algorithm Configuration IJCAI 2021
NAS-Bench-x11 and the Power of Learning Curves NIPS 2021
Well-tuned Simple Nets Excel on Tabular Datasets NIPS 2021
TempoRL: Learning When to Act ICML 2021
Understanding and Robustifying Differentiable Architecture Search ICLR 2020
Transferring Optimality Across Data Distributions via Homotopy Methods ICLR 2020
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search ICLR 2020
Meta-Learning of Neural Architectures for Few-Shot Learning CVPR 2020
Best Practices for Scientific Research on Neural Architecture Search JMLR 2020
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization ICLR 2020
AutoDispNet: Improving Disparity Estimation With AutoML ICCV 2019
Meta-Surrogate Benchmarking for Hyperparameter Optimization NIPS 2019
Learning to Design RNA ICLR 2019
An Evolution Strategy with Progressive Episode Lengths for Playing Games IJCAI 2019
Neural Architecture Search: A Survey JMLR 2019
NAS-Bench-101: Towards Reproducible Neural Architecture Search ICML 2019
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution ICLR 2019
Decoupled Weight Decay Regularization ICLR 2019
Maximizing acquisition functions for Bayesian optimization NIPS 2018
BOHB: Robust and Efficient Hyperparameter Optimization at Scale ICML 2018
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow ECCV 2018
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari IJCAI 2018
Neural Networks for Predicting Algorithm Runtime Distributions IJCAI 2018
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract) IJCAI 2017
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets AISTATS 2017
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA JMLR 2017
Towards Automatically-Tuned Neural Networks AUTOML 2016
Bayesian Optimization with Robust Bayesian Neural Networks NIPS 2016
Efficient and Robust Automated Machine Learning NIPS 2015
Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract) IJCAI 2015
Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves IJCAI 2015
On the Effective Configuration of Planning Domain Models IJCAI 2015
An Efficient Approach for Assessing Hyperparameter Importance ICML 2014
Bayesian Optimization in High Dimensions via Random Embeddings IJCAI 2013