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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 1851–1860 of 2050 papers

TitleStatusHype
Improved Group Robustness via Classifier Retraining on Independent SplitsCode0
Nearest Neighbour Equilibrium ClusteringCode0
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and SuggestionsCode0
BiasBed -- Rigorous Texture Bias EvaluationCode0
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized RecommendationsCode0
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy ModelsCode0
Pseudo-Labeling for Kernel Ridge Regression under Covariate ShiftCode0
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell TimeCode0
Improving Subseasonal Forecasting in the Western U.S. with Machine LearningCode0
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution ShiftCode0
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