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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 1876–1900 of 2050 papers

TitleStatusHype
Enhancing the Power of OOD Detection via Sample-Aware Model Selection—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Ensemble Reinforcement Learning: A Survey—0
Entropy-based Characterization of Modeling Constraints—0
Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19—0
Episodic memory for continual model learning—0
ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss—0
Error Reduction from Stacked Regressions—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Estimating Real Log Canonical Thresholds—0
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics—0
Estimating the Number of Components in Finite Mixture Models via Variational Approximation—0
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity—0
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach—0
Estimation of Local Average Treatment Effect by Data Combination—0
Estimation vs Metrics: is QE Useful for MT Model Selection?—0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions—0
Evaluating Gender Bias in Large Language Models—0
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts—0
Evaluating Meta-Regression Techniques: A Simulation Study on Heterogeneity in Location and Time—0
Evaluating Representations with Readout Model Switching—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
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