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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 876–900 of 2050 papers

TitleStatusHype
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!—0
EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge—0
Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities—0
Agreement-based Learning—0
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
Hierarchical Block Structures and High-resolution Model Selection in Large Networks—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
Causal Q-Aggregation for CATE Model Selection—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
High-Dimensional Dynamic Covariance Models with Random Forests—0
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality—0
Feedback-Controlled Sequential Lasso Screening—0
Causal Falling Rule Lists—0
Federated Model Search via Reinforcement Learning—0
High SNR Consistent Compressive Sensing—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Aggregation of Affine Estimators—0
Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
A coupled-mechanisms modelling framework for neurodegeneration—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
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