Frank Hutter

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