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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 576–600 of 2050 papers

TitleStatusHype
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Automated Dependence PlotsCode0
Diagnostic Tool for Out-of-Sample Model EvaluationCode0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
Have I done enough planning or should I plan more?Code0
Bayesian Joint Spike-and-Slab Graphical LassoCode0
Hierarchical clustering: visualization, feature importance and model selectionCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Deep Generalized Method of Moments for Instrumental Variable AnalysisCode0
How False Data Affects Machine Learning Models in Electrochemistry?Code0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
EPP: interpretable score of model predictive powerCode0
Differentiable Model Selection for Ensemble LearningCode0
Adaptive spline fitting with particle swarm optimizationCode0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
Impact of ImageNet Model Selection on Domain AdaptationCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Dirichlet process mixtures of block g priors for model selection and prediction in linear modelsCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Deep Bayesian Multi-Target Learning for Recommender SystemsCode0
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)Code0
Deep Active Learning with Adaptive AcquisitionCode0
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