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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 1071–1080 of 2050 papers

TitleStatusHype
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Inferring bias and uncertainty in camera calibration—0
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Order Book Queue Hawkes-Markovian Modeling—0
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space DecompositionCode1
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Model-Parallel Model Selection for Deep Learning Systems—0
Model Selection for Generic Reinforcement Learning—0
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