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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 1426–1450 of 2050 papers

TitleStatusHype
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection—0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Selective machine learning of doubly robust functionals—0
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image SegmentationCode1
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Global Adaptive Generative Adjustment—0
Enhancing Certifiable Robustness via a Deep Model Ensemble—0
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control—0
Model selection for deep audio source separation via clustering analysis—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations—0
Gaussian Mixture Clustering Using Relative Tests of Fit—0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
Distributed filtered hyperinterpolation for noisy data on the sphere—0
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries—0
Model Order Selection Based on Information Theoretic Criteria: Design of the Penalty—0
Exploiting BERT for End-to-End Aspect-based Sentiment AnalysisCode1
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Rejoinder on: Minimal penalties and the slope heuristics: a survey—0
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