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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 71–80 of 2050 papers

TitleStatusHype
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM SelectionCode1
Generative diffusion model surrogates for mechanistic agent-based biological models—0
Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework—0
Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare—0
A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection—0
OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis—0
Optimizing Hard Thresholding for Sparse Model Discovery—0
Evaluating Meta-Regression Techniques: A Simulation Study on Heterogeneity in Location and Time—0
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