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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 1176–1200 of 2050 papers

TitleStatusHype
Joint Continuous and Discrete Model Selection via Submodularity—0
Geostatistical Learning: Challenges and OpportunitiesCode0
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
Pareto Optimal Model Selection in Linear Bandits—0
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge—0
Derivative-Free Reinforcement Learning: A Review—0
Ising Model Selection Using _1-Regularized Linear Regression: A Statistical Mechanics Analysis—0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems—0
Vine copula mixture models and clustering for non-Gaussian dataCode0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Ranking vs. Classifying: Measuring Knowledge Base Completion QualityCode0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
A linearized framework and a new benchmark for model selection for fine-tuning—0
Sequential prediction under log-loss and misspecification—0
Choice modelling in the age of machine learning - discussion paper—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
A Characterization for Optimal Bundling of Products with Non-Additive Values—0
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
Cost-based feature selection for network model choice—0
Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability—0
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