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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 621–630 of 2050 papers

TitleStatusHype
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Bayesian Robust Tensor Factorization for Incomplete Multiway Data—0
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation—0
DSDE: Using Proportion Estimation to Improve Model Selection for Out-of-Distribution Detection—0
Bayesian stochastic blockmodeling—0
Bayesian leave-one-out cross-validation for large data—0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
Bayesian Learning with Wasserstein Barycenters—0
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