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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 281–290 of 2050 papers

TitleStatusHype
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
EPP: interpretable score of model predictive powerCode0
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution ShiftCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Differentiable Model Selection for Ensemble LearningCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
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