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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 401–410 of 2050 papers

TitleStatusHype
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive ModellingCode0
ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss—0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
Statistical inference for quantum singular models—0
An AutoML-based approach for Network Intrusion Detection—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
LLM4DS: Evaluating Large Language Models for Data Science Code Generation—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
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