
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO c...
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Differential Evolution for Neural Architecture Search
Neural architecture search (NAS) methods rely on a search strategy for d...
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Convergence Analysis of HomotopySGD for nonconvex optimization
Firstorder stochastic methods for solving largescale nonconvex optimi...
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Hyperparameter Transfer Across Developer Adjustments
After developer adjustments to a machine learning (ML) algorithm, how ca...
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On the Importance of Domain Model Configuration for Automated Planning Engines
The development of domainindependent planners within the AI Planning co...
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Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
In this paper, we propose an approach to neural architecture search (NAS...
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Neural Modelbased Optimization with RightCensored Observations
In many fields of study, we only observe lower bounds on the true respon...
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SampleEfficient Automated Deep Reinforcement Learning
Despite significant progress in challenging problems across various doma...
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NASBench301 and the Case for Surrogate Benchmarks for Neural Architecture Search
Neural Architecture Search (NAS) is a logical next step in the automatic...
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AutoSklearn 2.0: The Next Generation
Automated Machine Learning, which supports practitioners and researchers...
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Priorguided Bayesian Optimization
While Bayesian Optimization (BO) is a very popular method for optimizing...
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AutoPyTorch Tabular: MultiFidelity MetaLearning for Efficient and Robust AutoDL
While early AutoML frameworks focused on optimizing traditional ML pipel...
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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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Learning Heuristic Selection with Dynamic Algorithm Configuration
A key challenge in satisfying planning is to use multiple heuristics wit...
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On the Promise of the Stochastic Generalized GaussNewton Method for Training DNNs
Following early work on Hessianfree methods for deep learning, we study...
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MachineLearningBased Diagnostics of EEG Pathology
Machine learning (ML) methods have the potential to automate clinical EE...
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NASBench1Shot1: Benchmarking and Dissecting Oneshot Neural Architecture Search
Oneshot neural architecture search (NAS) has played a crucial role in m...
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MetaLearning of Neural Architectures for FewShot Learning
The recent progress in neural architectures search (NAS) has allowed sca...
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OpenMLPython: an extensible Python API for OpenML
OpenML is an online platform for open science collaboration in machine l...
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Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control
Current Deep Reinforcement Learning algorithms still heavily rely on han...
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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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Understanding and Robustifying Differentiable Architecture Search
Differentiable Architecture Search (DARTS) has attracted a lot of attent...
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!MDP Playground: MetaFeatures in Reinforcement Learning
Reinforcement Learning (RL) algorithms usually assume their environment ...
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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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Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
Bayesian Optimization (BO) is a common approach for hyperparameter optim...
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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 Whitebox Benchmarks for Algorithm Control
The performance of many algorithms in the fields of hard combinatorial p...
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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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Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
Due to the high computational demands executing a rigorous comparison be...
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MetaLearning Acquisition Functions for Bayesian Optimization
Many practical applications of machine learning require dataefficient b...
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NASBench101: Towards Reproducible Neural Architecture Search
Recent advances in neural architecture search (NAS) demand tremendous co...
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Learning to Design RNA
Designing RNA molecules has garnered recent interest in medicine, synthe...
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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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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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BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Modern deep learning methods are very sensitive to many hyperparameters,...
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Generative Reversible Networks
Generative models with an encoding component such as autoencoders curren...
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Training Generative Reversible Networks
Generative models with an encoding component such as autoencoders curren...
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Maximizing acquisition functions for Bayesian optimization
Bayesian optimization is a sampleefficient approach to global optimizat...
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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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Uncertainty Estimates for Optical Flow with MultiHypotheses Networks
Recent work has shown that optical flow estimation can be formulated as ...
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The reparameterization trick for acquisition functions
Bayesian optimization is a sampleefficient approach to solving global o...
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Fixing Weight Decay Regularization in Adam
We note that common implementations of adaptive gradient algorithms, suc...
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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 decoding and visualization of EEG pathology
We apply convolutional neural networks (ConvNets) to the task of disting...
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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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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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