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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 1511–1520 of 2050 papers

TitleStatusHype
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
INFaaS: A Model-less and Managed Inference Serving SystemCode0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
Lifelong Bayesian Optimization—0
Deep Generalized Method of Moments for Instrumental Variable AnalysisCode0
A Geometric Modeling of Occam's Razor in Deep Learning—0
Cold Case: The Lost MNIST DigitsCode0
Variational Inference for Sparse Gaussian Process Modulated Hawkes ProcessCode0
Model Validation Using Mutated Training Labels: An Exploratory Study—0
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