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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 521–530 of 2050 papers

TitleStatusHype
EPP: interpretable score of model predictive powerCode0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
Differentiable Model Selection for Ensemble LearningCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Batch Value-function Approximation with Only RealizabilityCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
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