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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 1551–1575 of 2050 papers

TitleStatusHype
On the Computation and Applications of Large Dense Partial Correlation Networks—0
Regularity Normalization: Constraining Implicit Space with Minimum Description Length—0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
Convex Covariate Clustering for ClassificationCode0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Machine learning in policy evaluation: new tools for causal inferenceCode0
On the complexity of logistic regression models—0
Unsupervised Attention Mechanism across Neural Network LayersCode0
Representation Learning with Weighted Inner Product for Universal Approximation of General SimilaritiesCode0
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning—0
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
Efficient Cross-Validation for Semi-Supervised Learning—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Model Selection for Simulator-based Statistical Models: A Kernel Approach—0
Un modèle Bayésien de co-clustering de données mixtes—0
Learning Counterfactual Representations for Estimating Individual Dose-Response CurvesCode0
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