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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 311–320 of 2050 papers

TitleStatusHype
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discoveryCode0
eGAD! double descent is explained by Generalized Aliasing Decomposition—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary ObjectivesCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection—0
The Mismeasure of Man and Models: Evaluating Allocational Harms in Large Language Models—0
Within-vector viral dynamics challenges how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya—0
A Dirichlet stochastic block model for composition-weighted networks—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
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