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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 701–725 of 2050 papers

TitleStatusHype
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Black-box Selective Inference via Bootstrapping—0
Differentially Private Learning with Margin Guarantees—0
Evaluating Meta-Regression Techniques: A Simulation Study on Heterogeneity in Location and Time—0
Differentially Private Generalized Linear Models Revisited—0
Evaluating Representations with Readout Model Switching—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
Evaluating the Utility of Model Explanations for Model Development—0
Bayesian Interpolation with Deep Linear Networks—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach—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
Adaptive Online Learning—0
Factor-Augmented Regularized Model for Hazard Regression—0
Exact Dimensionality Selection for Bayesian PCA—0
Exact Post Model Selection Inference for Marginal Screening—0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
DGSAC: Density Guided Sampling and Consensus—0
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