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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 111–120 of 2050 papers

TitleStatusHype
Unreflected Use of Tabular Data Repositories Can Undermine Research Quality—0
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks—0
Conformal Prediction with Upper and Lower Bound Models—0
Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
Ranking pre-trained segmentation models for zero-shot transferability—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Multi-model Stochastic Particle-based Variational Bayesian Inference for Multiband Delay Estimation—0
Forecasting Whole-Brain Neuronal Activity from Volumetric Video—0
Understanding the Limits of Deep Tabular Methods with Temporal Shift—0
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