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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 531–540 of 2050 papers

TitleStatusHype
Best of many worlds: Robust model selection for online supervised learning—0
Best of Both Worlds Model Selection—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models—0
A deep learning based solution for construction equipment detection: from development to deployment—0
Benchmarking the rationality of AI decision making using the transitivity axiom—0
Anomaly Detection for an E-commerce Pricing System—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—0
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks—0
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