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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 1151–1175 of 2050 papers

TitleStatusHype
SC-Safety: A Multi-round Open-ended Question Adversarial Safety Benchmark for Large Language Models in Chinese—0
Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases—0
Second-Order Convergence in Private Stochastic Non-Convex Optimization—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Segmentation et Interprétation de Nuages de Points pour la Modélisation d'Environnements Urbains—0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice—0
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge—0
Cost-based feature selection for network model choice—0
Selective Factor Extraction in High Dimensions—0
Selective Inference and Learning Mixed Graphical Models—0
Selective Inference for Latent Block Models—0
Selective linear segmentation for detecting relevant parameter changes—0
Selective Sequential Model Selection—0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Self-directed Machine Learning—0
Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models—0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy—0
SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection—0
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Sequential Model-Based Ensemble Optimization—0
Sequential prediction under log-loss and misspecification—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
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