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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 1101–1125 of 2050 papers

TitleStatusHype
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing FlowsCode0
Feature and Parameter Selection in Stochastic Linear Bandits—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
On the Use of Minimum Penalties in Statistical Learning—0
Bayesian Boosting for Linear Mixed Models—0
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
Encoding-dependent generalization bounds for parametrized quantum circuits—0
An Information-theoretic Approach to Distribution ShiftsCode1
AI without networks—0
Neural Active Learning with Performance Guarantees—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Network Estimation by Mixing: Adaptivity and More—0
Robust Model Selection and Nearly-Proper Learning for GMMs—0
Morphological Segmentation for SenecaCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Corpus-Based Paraphrase Detection Experiments and Review—0
Graph Similarity Description: How Are These Graphs Similar?—0
Model Selection for Production System via Automated Online Experiments—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Vector autoregression models with skewness and heavy tails—0
Informative Bayesian model selection for RR Lyrae star classifiers—0
True Few-Shot Learning with Language ModelsCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
Hypothesis Testing for Equality of Latent Positions in Random Graphs—0
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