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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 551–575 of 2050 papers

TitleStatusHype
Bayesian stochastic blockmodeling—0
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation—0
A data-centric approach to class-specific bias in image data augmentation—0
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial—0
Dominant Drivers of National Inflation—0
Bayesian Robust Tensor Factorization for Incomplete Multiway Data—0
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns—0
An Instrumental Variables Approach to Testing Firm Conduct—0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach—0
An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Bayesian optimization for automated model selection—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
Adaptive variational Bayes: Optimality, computation and applications—0
Gmail Smart Compose: Real-Time Assisted Writing—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection—0
Bayesian Network Models for Adaptive Testing—0
Bayesian Model Selection via Mean-Field Variational Approximation—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
Bayesian Model Selection of Stochastic Block Models—0
An information criterion for auxiliary variable selection in incomplete data analysis—0
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