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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 1826–1850 of 2050 papers

TitleStatusHype
Discovering Distribution Shifts using Latent Space RepresentationsCode0
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
Multiple testing for signal-agnostic searches of new physics with machine learningCode0
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced ClassificationCode0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
HybridSVD: When Collaborative Information is Not EnoughCode0
Dirichlet process mixtures of block g priors for model selection and prediction in linear modelsCode0
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN PerformanceCode0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning AlgorithmsCode0
Post-Selection Confidence Bounds for Prediction PerformanceCode0
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble SolutionCode0
Hyperparameter Tuning and Model Evaluation in Causal Effect EstimationCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level PerformancesCode0
BiasBed - Rigorous Texture Bias EvaluationCode0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Multi-view Deep Subspace Clustering NetworksCode0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect EstimatorsCode0
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility ReportCode0
Impact of ImageNet Model Selection on Domain AdaptationCode0
Exploring validation metrics for offline model-based optimisation with diffusion modelsCode0
Supervised Models Can Generalize Also When Trained on Random LabelCode0
Improved identification accuracy in equation learning via comprehensive R^2-elimination and Bayesian model selectionCode0
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