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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 761–770 of 2050 papers

TitleStatusHype
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
Fast and fully-automated histograms for large-scale data sets—0
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Out-of-sample scoring and automatic selection of causal estimatorsCode2
On the Complexity of Representation Learning in Contextual Linear Bandits—0
Dominant Drivers of National Inflation—0
Optimal Model Selection in RDD and Related Settings Using Placebo Zones—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—0
Stochastic Rising BanditsCode0
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