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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 341–350 of 2050 papers

TitleStatusHype
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
Causal Falling Rule Lists—0
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
Causal Q-Aggregation for CATE Model Selection—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
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