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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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TitleStatusHype
Differentially Private Learning with Margin Guarantees—0
Differentially Private Generalized Linear Models Revisited—0
Bayesian Interpolation with Deep Linear Networks—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage—0
e-Values for Real-Time Residential Electricity Demand Forecast Model Selection—0
Evasion Attacks against Machine Learning at Test Time—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach—0
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