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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 151–175 of 2050 papers

TitleStatusHype
Optimality in importance sampling: a gentle survey—0
The Value of Information in Human-AI Decision-making—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Mixture of neural operator experts for learning boundary conditions and model selection—0
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Scaling Inference-Efficient Language Models—0
Vision-Language Model Selection and Reuse for Downstream Adaptation—0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models—0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
Quantifying Uncertainty and Variability in Machine Learning: Confidence Intervals for Quantiles in Performance Metric Distributions—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-SpreadingCode0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Statistical Inference for Sequential Feature Selection after Domain AdaptationCode0
Principled model selection for stochastic dynamics—0
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring—0
Fast sampling and model selection for Bayesian mixture models—0
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences—0
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