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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 1601–1650 of 2050 papers

TitleStatusHype
Bayesian Active Model Selection with an Application to Automated Audiometry—0
Bayesian Adaptive Matrix Factorization With Automatic Model Selection—0
Bayesian Anomaly Detection and Classification—0
Bayesian Boosting for Linear Mixed Models—0
Bayesian CART models for insurance claims frequency—0
A Bayesian Model for Bivariate Causal Inference—0
Bayesian Evidence and Model Selection—0
Bayesian Hierarchical Community Discovery—0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
Bayesian Interpolation with Deep Linear Networks—0
Bayesian Learning with Wasserstein Barycenters—0
Bayesian leave-one-out cross-validation for large data—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Bayesian Model Selection for Change Point Detection and Clustering—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
Bayesian Model Selection Methods for Mutual and Symmetric k-Nearest Neighbor Classification—0
Bayesian Model Selection of Stochastic Block Models—0
Bayesian Model Selection via Mean-Field Variational Approximation—0
Bayesian Network Models for Adaptive Testing—0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
Bayesian optimization for automated model selection—0
Bayesian Optimization for Selecting Efficient Machine Learning Models—0
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach—0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns—0
Bayesian Robust Tensor Factorization for Incomplete Multiway Data—0
Bayesian stochastic blockmodeling—0
Bayesian Structure Learning by Recursive Bootstrap—0
Bayesian taut splines for estimating the number of modes—0
Bayesian Variable Selection for Globally Sparse Probabilistic PCA—0
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data—0
Belief propagation for permutations, rankings, and partial orders—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks—0
Benchmarking the rationality of AI decision making using the transitivity axiom—0
Best of Both Worlds Model Selection—0
Best of many worlds: Robust model selection for online supervised learning—0
Best Practices for Text Annotation with Large Language Models—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
Better Model Selection with a new Definition of Feature Importance—0
Beyond Conjugacy for Chain Event Graph Model Selection—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection—0
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Selective machine learning of doubly robust functionals—0
biastest: Testing parameter equality across different models in Stata—0
Binary Matrix Factorization via Dictionary Learning—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
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