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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 1051–1060 of 2050 papers

TitleStatusHype
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection—0
Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways—0
PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models—0
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples—0
Proximity Operator of the Matrix Perspective Function and its Applications—0
Pseudo Label Selection is a Decision Problem—0
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
Pushing the limits of fairness impossibility: Who's the fairest of them all?—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
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