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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 1971–1980 of 2050 papers

TitleStatusHype
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language ModelsCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
Deep Active Learning with Adaptive AcquisitionCode0
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary ObjectivesCode0
Learning Sparse Neural Networks through L_0 RegularizationCode0
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP ModelsCode0
The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data IntegrationCode0
On Statistical Efficiency in LearningCode0
Sparse Partially Linear Additive ModelsCode0
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