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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 211–220 of 2050 papers

TitleStatusHype
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
EPP: interpretable score of model predictive powerCode0
Differentiable Model Selection for Ensemble LearningCode0
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