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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 931–940 of 2050 papers

TitleStatusHype
Monitored Distillation for Positive Congruent Depth CompletionCode1
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice—0
Black-box Selective Inference via Bootstrapping—0
DeepDPM: Deep Clustering With an Unknown Number of ClustersCode2
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Rich Feature Construction for the Optimization-Generalization DilemmaCode1
Predictor Selection for Synthetic Controls—0
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging—0
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