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Found 23 papers, 19 papers with code
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation
We introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order.
Optimizing Hyperparameters with Conformal Quantile Regression
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search.
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications.
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints
We could show that for 8 out of 13 subjects, the proposed approach using Bayesian optimization succeeded to select the individually optimal SOA out of multiple evaluated SOA values.
Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio
Dynamic sparsity pruning undoes this limitation and allows to adapt the structure of the sparse neural network during training.
Automatic Termination for Hyperparameter Optimization
Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time.
Hyperparameter Transfer Learning with Adaptive Complexity
Bayesian optimization (BO) is a sample efficient approach to automatically tune the hyperparameters of machine learning models.
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods.
Model-based Asynchronous Hyperparameter and Neural Architecture Search
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based on training data with variable-length learning curves.
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve.
Towards Automatically-Tuned Deep Neural Networks
Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks.
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome.
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation.
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal.
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible.
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology.
RoBO: A Flexible and Robust Bayesian Optimization Framework in Python
Bayesian optimization is a powerful approach for the global derivative-free optimization of non-convex expensive functions.
Asynchronous Stochastic Gradient MCMC with Elastic Coupling
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling for problems where we can leverage (stochastic) gradients to define continuous dynamics which explore the target distribution.
Bayesian Optimization with Robust Bayesian Neural Networks
Bayesian optimization is a prominent method for optimizing expensive to evaluate black-box functions that is prominently applied to tuning the hyperparameters of machine learning algorithms.
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.
Efficient and Robust Automated Machine Learning
The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts.
Supplementary Material for Efficient and Robust Automated Machine Learning
Supplementary Material for Efficient and Robust Automated Machine Learning
