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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 351–360 of 2050 papers

TitleStatusHype
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge DistillationCode0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
Anytime Model Selection in Linear BanditsCode0
Bolasso: model consistent Lasso estimation through the bootstrapCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Fast Instrument Learning with Faster RatesCode0
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender SystemsCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
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