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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 1626–1650 of 2050 papers

TitleStatusHype
MEDFAIR: Benchmarking Fairness for Medical ImagingCode0
Algebraic Equivalence of Linear Structural Equation ModelsCode0
Anytime Model Selection in Linear BanditsCode0
Change point detection for graphical models in the presence of missing valuesCode0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Stochastic Marginal Likelihood Gradients using Neural Tangent KernelsCode0
Differentiable Model Selection for Ensemble LearningCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Stochastic Rising BanditsCode0
MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic CommunicationCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Vine copula mixture models and clustering for non-Gaussian dataCode0
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal TransportCode0
metboost: Exploratory regression analysis with hierarchically clustered dataCode0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
metric-learn: Metric Learning Algorithms in PythonCode0
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in BuildingsCode0
MGTCOM: Community Detection in Multimodal GraphsCode0
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
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