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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 671–680 of 2050 papers

TitleStatusHype
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
Enhancing the Power of OOD Detection via Sample-Aware Model Selection—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Achieving Fairness with a Simple Ridge Penalty—0
Ensemble Reinforcement Learning: A Survey—0
Differentially Private Learning with Margin Guarantees—0
Entropy-based Characterization of Modeling Constraints—0
Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19—0
Differentially Private Generalized Linear Models Revisited—0
Bayesian Interpolation with Deep Linear Networks—0
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