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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 921–930 of 2050 papers

TitleStatusHype
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
LEATHER: A Framework for Learning to Generate Human-like Text in DialogueCode0
On the calibration of underrepresented classes in LiDAR-based semantic segmentation—0
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble LearningCode0
To tree or not to tree? Assessing the impact of smoothing the decision boundaries—0
Multi-View Independent Component Analysis with Shared and Individual Sources—0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
MEDFAIR: Benchmarking Fairness for Medical ImagingCode0
Detection and Evaluation of Clusters within Sequential Data—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
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