
AutoPyTorch Tabular: MultiFidelity MetaLearning for Efficient and Robust AutoDL
While early AutoML frameworks focused on optimizing traditional ML pipel...
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Priorguided Bayesian Optimization
While Bayesian Optimization (BO) is a very popular method for optimizing...
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Neural Ensemble Search for Performant and Calibrated Predictions
Ensembles of neural networks achieve superior performance compared to st...
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MachineLearningBased Diagnostics of EEG Pathology
Machine learning (ML) methods have the potential to automate clinical EE...
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Generative Reversible Networks
Generative models with an encoding component such as autoencoders curren...
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Simple And Efficient Architecture Search for Convolutional Neural Networks
Neural networks have recently had a lot of success for many tasks. Howev...
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Predicting Runtime Distributions using Deep Neural Networks
Many stateoftheart algorithms for solving hard combinatorial problems...
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Warmstarting of Modelbased Algorithm Configuration
The performance of many hard combinatorial problem solvers depends stron...
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Deep learning with convolutional neural networks for EEG decoding and visualization
A revised version of this article is now available at Human Brain Mappin...
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SGDR: Stochastic Gradient Descent with Warm Restarts
Restart techniques are common in gradientfree optimization to deal with...
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Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
We apply convolutional neural networks (ConvNets) to the task of disting...
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CMAES for Hyperparameter Optimization of Deep Neural Networks
Hyperparameters of deep neural networks are often optimized by grid sear...
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OpenML Benchmarking Suites and the OpenML100
We advocate the use of curated, comprehensive benchmark suites of machin...
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Pitfalls and Best Practices in Algorithm Configuration
Good parameter settings are crucial to achieve high performance in many ...
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Online Batch Selection for Faster Training of Neural Networks
Deep neural networks are commonly trained using stochastic nonconvex op...
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A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
The original ImageNet dataset is a popular largescale benchmark for tra...
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Asynchronous Stochastic Gradient MCMC with Elastic Coupling
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampli...
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Efficient Benchmarking of Algorithm Configuration Procedures via ModelBased Surrogates
The optimization of algorithm (hyper)parameters is crucial for achievin...
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A case study of algorithm selection for the traveling thief problem
Many realworld problems are composed of several interacting components....
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The reparameterization trick for acquisition functions
Bayesian optimization is a sampleefficient approach to solving global o...
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ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a se...
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The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for differ...
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Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Bayesian optimization has become a successful tool for hyperparameter op...
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ParamILS: An Automatic Algorithm Configuration Framework
The identification of performanceoptimizing parameter settings is an im...
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SATzilla: Portfoliobased Algorithm Selection for SAT
It has been widely observed that there is no single "dominant" SAT solve...
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Bayesian Optimization With Censored Response Data
Bayesian optimization (BO) aims to minimize a given blackbox function us...
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Bayesian Optimization in a Billion Dimensions via Random Embeddings
Bayesian optimization techniques have been successfully applied to robot...
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Algorithm Runtime Prediction: Methods & Evaluation
Perhaps surprisingly, it is possible to predict how long an algorithm wi...
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Fixing Weight Decay Regularization in Adam
We note that common implementations of adaptive gradient algorithms, suc...
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Uncertainty Estimates for Optical Flow with MultiHypotheses Networks
Recent work has shown that optical flow estimation can be formulated as ...
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Multiobjective Architecture Search for CNNs
Architecture search aims at automatically finding neural architectures t...
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Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
Evolution Strategies (ES) have recently been demonstrated to be a viable...
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Maximizing acquisition functions for Bayesian optimization
Bayesian optimization is a sampleefficient approach to global optimizat...
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BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Modern deep learning methods are very sensitive to many hyperparameters,...
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Training Generative Reversible Networks
Generative models with an encoding component such as autoencoders curren...
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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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Neural Architecture Search: A Survey
Deep Learning has enabled remarkable progress over the last years on a v...
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Learning to Design RNA
Designing RNA molecules has garnered recent interest in medicine, synthe...
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NASBench101: Towards Reproducible Neural Architecture Search
Recent advances in neural architecture search (NAS) demand tremendous co...
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MetaLearning Acquisition Functions for Bayesian Optimization
Many practical applications of machine learning require dataefficient b...
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Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
Due to the high computational demands executing a rigorous comparison be...
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MetaSurrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), availa...
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AutoDispNet: Improving Disparity Estimation with AutoML
Much research work in computer vision is being spent on optimizing exist...
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Towards Whitebox Benchmarks for Algorithm Control
The performance of many algorithms in the fields of hard combinatorial p...
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BOAH: A Tool Suite for MultiFidelity Bayesian Optimization & Analysis of Hyperparameters
Hyperparameter optimization and neural architecture search can become pr...
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Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
Bayesian Optimization (BO) is a common approach for hyperparameter optim...
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Best Practices for Scientific Research on Neural Architecture Search
We describe a set of best practices for the young field of neural archit...
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!MDP Playground: MetaFeatures in Reinforcement Learning
Reinforcement Learning (RL) algorithms usually assume their environment ...
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Understanding and Robustifying Differentiable Architecture Search
Differentiable Architecture Search (DARTS) has attracted a lot of attent...
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Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
We propose probabilistic models that can extrapolate learning curves of ...
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