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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 1111–1120 of 2050 papers

TitleStatusHype
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
Revealing consensus and dissensus between network partitions—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search—0
Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
Risk Aware Benchmarking of Large Language Models—0
Risk-consistency of cross-validation with lasso-type procedures—0
Risk-Controlling Model Selection via Guided Bayesian Optimization—0
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems—0
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