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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 771–780 of 2050 papers

TitleStatusHype
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
The Interpolating Information Criterion for Overparameterized Models—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Risk Controlled Image RetrievalCode0
Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models—0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
Online Laplace Model Selection Revisited—0
Bayesian taut splines for estimating the number of modes—0
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts—0
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