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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 776–800 of 2050 papers

TitleStatusHype
The smooth output assumption, and why deep networks are better than wide ones—0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
BiasBed -- Rigorous Texture Bias EvaluationCode0
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks—0
cegpy: Modelling with Chain Event Graphs in PythonCode1
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models—0
Exploring validation metrics for offline model-based optimisation with diffusion modelsCode0
Understanding the double descent curve in Machine Learning—0
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks—0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves—0
Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great BritainCode1
Robust Model Selection of Gaussian Graphical Models—0
MGTCOM: Community Detection in Multimodal GraphsCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Beyond Conjugacy for Chain Event Graph Model Selection—0
scikit-fda: A Python Package for Functional Data AnalysisCode2
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?—0
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
Toward Unsupervised Outlier Model SelectionCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
Oracle Inequalities for Model Selection in Offline Reinforcement Learning—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
Differentiable Model Selection for Ensemble LearningCode0
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