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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 1911–1920 of 2050 papers

TitleStatusHype
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model SelectionCode0
NYTRO: When Subsampling Meets Early StoppingCode0
DeepNNK: Explaining deep models and their generalization using polytope interpolationCode0
Iterative Hard Thresholding for Model Selection in Genome-Wide Association StudiesCode0
Finding Materialized Models for Model ReuseCode0
Combining Model and Parameter Uncertainty in Bayesian Neural NetworksCode0
Joint Inference for Neural Network Depth and Dropout RegularizationCode0
Odd-One-Out Representation LearningCode0
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT RankersCode0
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly DetectionCode0
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