Hutter, Frank

Decoupled Weight Decay Regularization

Loshchilov, I., & Hutter, F. (2017) · arXiv

Stochastic Gradient Optimization TechniquesDomain Adaptation and Few-Shot Learning

Cited by 12,522

SGDR: Stochastic Gradient Descent with Warm Restarts

Loshchilov, I., & Hutter, F. (2016) · arXiv

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 5,309

Deep learning with convolutional neural networks for EEG decoding and visualization

Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping

EEG and Brain-Computer InterfacesEmotion and Mood Recognition

Cited by 3,751

Sequential Model-Based Optimization for General Algorithm Configuration

Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011) · Lecture Notes in Computer Science

Metaheuristic Optimization Algorithms ResearchMachine Learning and Data Classification

Cited by 2,469

Neural Architecture Search: A Survey

Elsken, T., Metzen, J. H., & Hutter, F. (2018) · arXiv

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 1,763

Hyperparameter Optimization

Feurer, M., & Hutter, F. (2019) · The Springer Series on Challenges in Machine Learning

Machine Learning and Data ClassificationAdvanced Bandit Algorithms Research

Cited by 1,414

Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms

Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Machine Learning and Data ClassificationAdvanced Bandit Algorithms Research

Cited by 1,359

Neural Architecture Search

Elsken, T., Metzen, J. H., & Hutter, F. (2019) · The Springer Series on Challenges in Machine Learning

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 785

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

Falkner, S., Klein, A., & Hutter, F. (2018) · arXiv

Advanced Bandit Algorithms ResearchMachine Learning and Data Classification

Cited by 551

Ai for social good: unlocking the opportunity for positive impact

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. V. D., Mugisha, F., et al. (2020) · Nature Communications

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 357

Managing extreme AI risks amid rapid progress

Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y. N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., et al. (2024) · Science

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 349

Efficient multi-objective neural architecture search via Lamarckian evolution

Elsken, T., Metzen, J. H., & Hutter, F. (2018) · arXiv

Evolutionary Algorithms and ApplicationsAdvanced Neural Network Applications

Cited by 311

NAS-bench-101: Towards reproducible neural architecture search

Ying, C., Klein, A., Real, E., Christiansen, E., Murphy, K., & Hutter, F. (2019) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 297

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhopf, T., Sass, R., & Hutter, F. (2021) · arXiv

Machine Learning and Data ClassificationAdvanced Bandit Algorithms Research

Cited by 195

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Lehman, J., Clune, J., Misevic, D., Adami, C., Altenberg, L., Beaulieu, J., Bentley, P. J., Bernard, S., Beslon, G., Bryson, D. M., et al. (2020) · Artificial Life

Evolutionary Algorithms and ApplicationsEvolution and Genetic Dynamics

Cited by 181

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

Zhang, B., Rajan, R., Pineda, L., Lambert, N., Biedenkapp, A., Chua, K., Hutter, F., & Calandra, R. (2021) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Data Classification

Cited by 53

TabArena: A Living Benchmark for Machine Learning on Tabular Data

Erickson, N., Purucker, L., Tschalzev, A., Holzmüller, D., Desai, P. M., Salinas, D., & Hutter, F. (2025) · arXiv

Machine Learning and Data ClassificationData Mining and Machine Learning Applications

Cited by 13

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues

Grazzi, R., Siems, J., Zela, A., Franke, J. K. H., Hutter, F., & Pontil, M. (2024) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 1