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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 621–630 of 2050 papers

TitleStatusHype
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Adaptive debiased machine learning using data-driven model selection techniques—0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Self-Compatibility: Evaluating Causal Discovery without Ground TruthCode1
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous drivingCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
The Interpolating Information Criterion for Overparameterized Models—0
Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Risk Controlled Image RetrievalCode0
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