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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 691–700 of 2050 papers

TitleStatusHype
Changing the Kernel During Training Leads to Double Descent in Kernel RegressionCode0
An energy-based comparative analysis of common approaches to text classification in the Legal domain—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models—0
NoMoPy: Noise Modeling in Python—0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Evaluating LLP Methods: Challenges and ApproachesCode0
Approximate Leave-one-out Cross Validation for Regression with _1 Regularizers (extended version)—0
Causal Q-Aggregation for CATE Model Selection—0
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
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