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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 1126–1150 of 2050 papers

TitleStatusHype
Laplace Redux - Effortless Bayesian Deep Learning—0
Towards Model Selection using Learning Curve Cross-ValidationCode0
On-the-fly learning of adaptive strategies with bandit algorithms—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
Achieving Fairness with a Simple Ridge Penalty—0
mikropml: User-Friendly R Package for Supervised Machine Learning PipelinesCode1
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming—0
Unsupervised Offline Changepoint Detection EnsemblesCode1
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Model Selection CNN-based VVC QualityEnhancement—0
Order flow in the financial markets from the perspective of the Fractional Lévy stable motion—0
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation—0
Synthetic Data for Model Selection—0
SMLSOM: The shrinking maximum likelihood self-organizing mapCode0
The Future of Employment Revisited: How Model Selection Determines Automation Forecasts—0
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT RankersCode0
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity—0
Scalable Marginal Likelihood Estimation for Model Selection in Deep LearningCode0
Where and What? Examining Interpretable Disentangled RepresentationsCode1
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
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