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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 331–340 of 2050 papers

TitleStatusHype
Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models—0
Moshi Moshi? A Model Selection Hijacking Adversarial Attack—0
biastest: Testing parameter equality across different models in Stata—0
Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMsCode0
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient EvaluationCode0
OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment—0
The Majority Vote Paradigm Shift: When Popular Meets Optimal—0
Model selection for behavioral learning data and applications to contextual bandits—0
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language ModelsCode0
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
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