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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 521–530 of 2050 papers

TitleStatusHype
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
Beyond Conjugacy for Chain Event Graph Model Selection—0
AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NER—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
An Optimal Likelihood Free Method for Biological Model Selection—0
Better Model Selection with a new Definition of Feature Importance—0
Designing Ecosystems of Intelligence from First Principles—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
Best Practices for Text Annotation with Large Language Models—0
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