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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
Federated Model Search via Reinforcement Learning—0
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss FunctionsCode0
Model Selection for Generic Contextual Bandits—0
Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies—0
A multi-stage machine learning model on diagnosis of esophageal manometry—0
Practical Transferability Estimation for Image Classification Tasks—0
Towards Transferable Adversarial Perturbations with Minimum Norm—0
Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernel—0
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation—0
Last Layer Marginal Likelihood for Invariance LearningCode0
Model Selection for Bayesian AutoencodersCode0
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
Time Series Anomaly Detection with label-free Model Selection—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing FlowsCode0
Feature and Parameter Selection in Stochastic Linear Bandits—0
Bayesian Boosting for Linear Mixed Models—0
On the Use of Minimum Penalties in Statistical Learning—0
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
AI without networks—0
Encoding-dependent generalization bounds for parametrized quantum circuits—0
Neural Active Learning with Performance Guarantees—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
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