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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 751–800 of 2050 papers

TitleStatusHype
General Hannan and Quinn Criterion for Common Time Series—0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Factor-Augmented Regularized Model for Hazard Regression—0
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces—0
Factors in Fashion: Factor Analysis towards the Mode—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
DGSAC: Density Guided Sampling and Consensus—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic—0
Fast and fully-automated histograms for large-scale data sets—0
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Carbon Intensity-Aware Adaptive Inference of DNNs—0
Bayesian Hierarchical Community Discovery—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
Fast Linear Model Trees by PILOT—0
Fast model selection by limiting SVM training times—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
Fast rates with high probability in exp-concave statistical learning—0
Fast sampling and model selection for Bayesian mixture models—0
Cats & Co: Categorical Time Series Coclustering—0
Feature-based model selection for object detection from point cloud data—0
Feature Selection Methods for Cost-Constrained Classification in Random Forests—0
Improving classification performance by feature space transformations and model selection—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
Federated Model Search via Reinforcement Learning—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Feedback-Controlled Sequential Lasso Screening—0
Causal Falling Rule Lists—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Generalized Information Criteria for Structured Sparse Models—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Greedy equivalence search for nonparametric graphical models—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
Bayesian Evidence and Model Selection—0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
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