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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 131–140 of 2050 papers

TitleStatusHype
Moshi Moshi? A Model Selection Hijacking Adversarial Attack—0
Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMsCode0
Optimizing Model Selection for Compound AI SystemsCode2
OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment—0
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient EvaluationCode0
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language ModelsCode0
Model selection for behavioral learning data and applications to contextual bandits—0
The Majority Vote Paradigm Shift: When Popular Meets Optimal—0
On Creating a Causally Grounded Usable Rating Method for Assessing the Robustness of Foundation Models Supporting Time Series—0
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
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