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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 1561–1570 of 2050 papers

TitleStatusHype
Automated Model Selection with Bayesian Quadrature—0
Logistic principal component analysis via non-convex singular value thresholding—0
Deep Bayesian Multi-Target Learning for Recommender SystemsCode0
Anomaly Detection for an E-commerce Pricing System—0
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributionsCode0
Bayesian Anomaly Detection and Classification—0
An information criterion for auxiliary variable selection in incomplete data analysis—0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Separating common (global and local) and distinct variation in multiple mixed types data setsCode0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
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