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06/29/2023
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation
We introduce ordered transfer hyperparameter optimisation (OTHPO), a ver...
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05/05/2023
Optimizing Hyperparameters with Conformal Quantile Regression
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely ...
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09/14/2021
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
To achieve peak predictive performance, hyperparameter optimization (HPO...
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08/26/2021
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints
The decoding of brain signals recorded via, e.g., an electroencephalogra...
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04/16/2021
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution
Bayesian Optimization (BO) is a successful methodology to tune the hyper...
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02/25/2021
Hyperparameter Transfer Learning with Adaptive Complexity
Bayesian optimization (BO) is a sample efficient approach to automatical...
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02/17/2021
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used b...
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03/24/2020
Model-based Asynchronous Hyperparameter Optimization
We introduce a model-based asynchronous multi-fidelity hyperparameter op...
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10/10/2019
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
We propose probabilistic models that can extrapolate learning curves of ...
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05/30/2019
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), availa...
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05/13/2019
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
Due to the high computational demands executing a rigorous comparison be...
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02/25/2019
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Recent advances in neural architecture search (NAS) demand tremendous co...
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07/18/2018
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
While existing work on neural architecture search (NAS) tunes hyperparam...
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07/04/2018
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Modern deep learning methods are very sensitive to many hyperparameters,...
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02/20/2018
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks
Recent work has shown that optical flow estimation can be formulated as ...
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12/02/2016
Asynchronous Stochastic Gradient MCMC with Elastic Coupling
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampli...
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05/23/2016
