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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 781–790 of 2050 papers

TitleStatusHype
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Active Comparison of Prediction Models—0
A Bayesian Perspective on Training Speed and Model Selection—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Clipper: A Low-Latency Online Prediction Serving System—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Classification with Sparse Overlapping Groups—0
On The Stability of Interpretable Models—0
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