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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 1841–1850 of 2050 papers

TitleStatusHype
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
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
Dynamic Model Selection for Prediction Under a Budget—0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
Eagle: Efficient Training-Free Router for Multi-LLM Inference—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
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