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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 1141–1150 of 2050 papers

TitleStatusHype
Saturn: Efficient Multi-Large-Model Deep Learning—0
Scalable Bayesian Transformed Gaussian Processes—0
Scalable Model Selection for Belief Networks—0
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks—0
Scaling Inference-Efficient Language Models—0
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments—0
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics—0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
SCAMS: Simultaneous Clustering and Model Selection—0
Score-based Causal Learning in Additive Noise Models—0
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