Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms
Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms. Additional i…
Deep LearningHyperparameter OptimizationProvably tuning the ElasticNet across instances
An important unresolved challenge in the theory of regularization is to set the regularization coefficients of popular techniques like the ElasticNet with general provable guarantees. We consider the problem of tuning th…
Hyperparameter OptimizationregressionPASHA: Efficient HPO and NAS with Progressive Resource Allocation
Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run. When models are trained on larg…
BIG-bench Machine LearningHyperparameter OptimizationNeural Architecture SearchGoal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning
Tackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly impact their performances. In this work, we study the use of goal-orie…
BIG-bench Machine LearningDeep LearningHyperparameter OptimizationSensitivityStart Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data. Prior studies have shown that KGE models are sensitive to hyperparameter settings, however,…
Graph EmbeddingHyperparameter OptimizationKnowledge Graph EmbeddingKnowledge Graph Embeddings+2Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection
Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully ap…
Bayesian OptimizationHyperparameter OptimizationIntrusion DetectionNetwork Intrusion DetectionACHO: Adaptive Conformal Hyperparameter Optimization
Several novel frameworks for hyperparameter search have emerged in the last decade, but most rely on strict, often normal, distributional assumptions, limiting search model flexibility. This paper proposes a novel optimi…
Conformal PredictionHyperparameter OptimizationBetty: An Automatic Differentiation Library for Multilevel Optimization
Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neural architecture search and reinforcement …
GPUHyperparameter OptimizationMeta-LearningNeural Architecture SearchUsing Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems
In this paper we consider the machine learning (ML) task of predicting tipping point transitions and long-term post-tipping-point behavior associated with the time evolution of an unknown (or partially unknown), non-stat…
Hyperparameter OptimizationTime Series AnalysisAsynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization
Bayesian optimization (BO) is a promising approach for hyperparameter optimization of deep neural networks (DNNs), where each model training can take minutes to hours. In BO, a computationally cheap surrogate model is em…
Bayesian OptimizationComputational EfficiencyHyperparameter OptimizationBayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach
The rising growth of deep neural networks (DNNs) and datasets in size motivates the need for efficient solutions for simultaneous model selection and training. Many methods for hyperparameter optimization (HPO) of iterat…
Bayesian OptimizationHyperparameter OptimizationModel SelectionFEATHERS: Federated Architecture and Hyperparameter Search
Deep neural architectures have profound impact on achieved performance in many of today's AI tasks, yet, their design still heavily relies on human prior knowledge and experience. Neural architecture search (NAS) togethe…
AutoMLBIG-bench Machine LearningFederated LearningHyperparameter Optimization+2Prediction of Football Player Value using Bayesian Ensemble Approach
The transfer fees of sports players have become astronomical. This is because bringing players of great future value to the club is essential for their survival. We present a case study on the key factors affecting the w…
Hyperparameter OptimizationregressionSTREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison
Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools/packages (e.g. scikit-learn) have been developed t…
AutoMLBinary ClassificationFeature Importancefeature selection+2Near-optimal control of dynamical systems with neural ordinary differential equations
Optimal control problems naturally arise in many scientific applications where one wishes to steer a dynamical system from a certain initial state $\mathbf{x}_0$ to a desired target state $\mathbf{x}^*$ in finite time $T…
Hyperparameter OptimizationMulti-Objective Hyperparameter Optimization in Machine Learning -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal pe…
Bayesian OptimizationBIG-bench Machine LearningEvolutionary AlgorithmsFairness+1Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution
Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which leads to the selected confi…
Bayesian OptimizationHyperparameter OptimizationInterpretable Machine LearningFlexible Differentiable Optimization via Model Transformations
We introduce DiffOpt.jl, a Julia library to differentiate through the solution of optimization problems with respect to arbitrary parameters present in the objective and/or constraints. The library builds upon MathOptInt…
Hyperparameter OptimizationmodelFedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization
Hyperparameter optimization (HPO) is crucial for machine learning algorithms to achieve satisfactory performance, whose progress has been boosted by related benchmarks. Nonetheless, existing efforts in benchmarking all f…
BenchmarkingFederated LearningHyperparameter OptimizationClick prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines
Online travel agencies (OTA's) advertise their website offers on meta-search bidding engines. The problem of predicting the number of clicks a hotel would receive for a given bid amount is an important step in the manage…
Dimensionality ReductionEnsemble LearningHyperparameter OptimizationManagement