SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1411–1420 of 2050 papers

TitleStatusHype
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
On hyperparameter tuning in general clustering problemsm—0
Meta-Learning PAC-Bayes Priors in Model Averaging—0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
Learning high-dimensional probability distributions using tree tensor networks—0
Noise Fit, Estimation Error and a Sharpe Information Criterion—0
Forward and Backward Feature Selection for Query Performance Prediction—0
Bayesian Model Selection for Change Point Detection and Clustering—0
Automated Dependence PlotsCode0
Show:102550
← PrevPage 142 of 205Next →

No leaderboard results yet.