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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 1411–1420 of 2050 papers

TitleStatusHype
Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
A Systematic Analysis of Base Model Choice for Reward Modeling—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
Nonlinear Causal Discovery for Grouped Data—0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models—0
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
AaltoNLP at SemEval-2022 Task 11: Ensembling Task-adaptive Pretrained Transformers for Multilingual Complex NER—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
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