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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 981–990 of 2050 papers

TitleStatusHype
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Factors in Fashion: Factor Analysis towards the Mode—0
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Model Selection for Inverse Reinforcement Learning via Structural Risk Minimization—0
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?—0
Factor-Augmented Regularized Model for Hazard Regression—0
Face Recognition using Optimal Representation Ensemble—0
Is F_1 Score Suboptimal for Cybersecurity Models? Introducing C_score, a Cost-Aware Alternative for Model Assessment—0
Face Recognition Using Deep Multi-Pose Representations—0
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