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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 201–210 of 2050 papers

TitleStatusHype
Evaluating Language Models as Synthetic Data GeneratorsCode1
Noncommutative Model Selection and the Data-Driven Estimation of Real Cohomology Groups—0
Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy—0
On the relative performance of some parametric and nonparametric estimators of option prices—0
SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
Optimized Conformal Selection: Powerful Selective Inference After Conformity Score OptimizationCode0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss—0
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive ModellingCode0
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