Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Interpretable label-free self-guided subspace clustering
Majority subspace clustering (SC) algorithms depend on one or more hyperparameters that need to be carefully tuned for the SC algorithms to achieve high clustering performance. Hyperparameter optimization (HPO) is often …
ClusteringHyperparameter OptimizationRecursive Gaussian Process State Space Model
Learning dynamical models from data is not only fundamental but also holds great promise for advancing principle discovery, time-series prediction, and controller design. Among various approaches, Gaussian Process State-…
Computational EfficiencyHyperparameter OptimizationmodelState Space Models+1Exploring the Manifold of Neural Networks Using Diffusion Geometry
Drawing motivation from the manifold hypothesis, which posits that most high-dimensional data lies on or near low-dimensional manifolds, we apply manifold learning to the space of neural networks. We learn manifolds wher…
Dimensionality ReductionHyperparameter OptimizationNeural Architecture SearchDifferent Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML
With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often compared against each other to find the best method. These benchmarking efforts …
BenchmarkingFairnessfeature selectionHyperparameter OptimizationCross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting
Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their …
Computational EfficiencyHyperparameter OptimizationScientific machine learning in ecological systems: A study on the predator-prey dynamics
In this study, we apply two pillars of Scientific Machine Learning: Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Lotka Volterra Predator Prey Model, a fundamenta…
Hyperparameter OptimizationLarge Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, …
AutoMLFeature EngineeringHyperparameter OptimizationModel SelectionAutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown promise, their training or implementation…
AutoMLHyperparameter OptimizationLanguage ModelingLanguage Modelling+2Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional …
Active LearningBayesian OptimizationDrug DiscoveryHyperparameter OptimizationComputation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable approximation error. Recent work accounts f…
Decision MakingGaussian ProcessesGPUHyperparameter Optimization+1EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
Conventional methods for Bayesian optimization (BO) primarily involve one-step optimal decisions (e.g., maximizing expected improvement of the next step). To avoid myopic behavior, multi-step lookahead BO algorithms such…
Bayesian OptimizationDecision MakingHyperparameter OptimizationReinforcement Learning (RL)+1Hyperparameter Optimization in Machine Learning
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effec…
Hyperparameter OptimizationMeta-LearningNeural Architecture SearchSequential Large Language Model-Based Hyper-parameter Optimization
This study introduces SLLMBO, an innovative framework leveraging large language models (LLMs) for hyperparameter optimization (HPO), incorporating dynamic search space adaptability, enhanced parameter space exploitation,…
Bayesian OptimizationBenchmarkingHyperparameter Optimizationimage-classification+6How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
We investigate the efficacy of data augmentations to close the domain gap in spaceborne computer vision, crucial for autonomous operations like on-orbit servicing. As the use of computer vision in space increases, challe…
2D Object DetectionDomain GeneralizationHyperparameter Optimizationobject-detection+1A comparative study of NeuralODE and Universal ODE approaches to solving Chandrasekhar White Dwarf equation
In this study, we apply two pillars of Scientific Machine Learning: Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Chandrasekhar White Dwarf Equation (CWDE). The C…
AstronomyHyperparameter OptimizationTesting the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting
Accurate forecasting of electrical demand is essential for maintaining a stable and reliable power grid, optimizing the allocation of energy resources, and promoting efficient energy consumption practices. This study inv…
Bayesian OptimizationHyperparameter OptimizationLoad ForecastingPredicting from Strings: Language Model Embeddings for Bayesian Optimization
Bayesian Optimization is ubiquitous in the field of experimental design and blackbox optimization for improving search efficiency, but has been traditionally restricted to regression models which are only applicable to f…
Bayesian OptimizationExperimental DesignHyperparameter OptimizationLanguage Modeling+2A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning
We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning, invariance learning and more. These…
Few-Shot LearningHyperparameter OptimizationMeta-LearningStochastic OptimizationOWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient
The physicochemical properties of chemical compounds have great importance in several areas, including pharmaceuticals, environmental and separation science. Among these are physicochemical properties such as the octanol…
Drug DiscoveryHyperparameter OptimizationAutomating Data Science Pipelines with Tensor Completion
Hyperparameter optimization is an essential component in many data science pipelines and typically entails exhaustive time and resource-consuming computations in order to explore the combinatorial search space. Similar t…
Hyperparameter OptimizationNeural Architecture Search