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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 1451–1475 of 2050 papers

TitleStatusHype
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
Model selection for deep audio source separation via clustering analysis—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations—0
Gaussian Mixture Clustering Using Relative Tests of Fit—0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
Distributed filtered hyperinterpolation for noisy data on the sphere—0
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries—0
Model Order Selection Based on Information Theoretic Criteria: Design of the Penalty—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Rejoinder on: Minimal penalties and the slope heuristics: a survey—0
On summarized validation curves and generalization—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
Interpretable Models for Understanding Immersive Simulations—0
The column measure and Gradient-Free Gradient Boosting—0
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis—0
How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions—0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
Not again! Data Leakage in Digital Pathology—0
Predictive Multiplicity in ClassificationCode0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
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