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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 541–550 of 2050 papers

TitleStatusHype
Evaluating LLP Methods: Challenges and ApproachesCode0
Approximate Leave-one-out Cross Validation for Regression with _1 Regularizers (extended version)—0
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
Causal Q-Aggregation for CATE Model Selection—0
mage based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimizationCode0
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption—0
GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model AgentsCode0
Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data—0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System—0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
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