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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 1701–1725 of 2050 papers

TitleStatusHype
Learning Sparse Neural Networks through L_0 RegularizationCode0
Episodic memory for continual model learning—0
Regularized Modal Regression with Applications in Cognitive Impairment Prediction—0
Scalable Model Selection for Belief Networks—0
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets—0
Nonparametric Independence Screening via Favored Smoothing Bandwidth—0
Population Based Training of Neural NetworksCode1
Parameter Reference Loss for Unsupervised Domain Adaptation—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
Network Model Selection Using Task-Focused Minimum Description Length—0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
Nonsparse learning with latent variables—0
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics—0
DAGGER: A sequential algorithm for FDR control on DAGsCode0
The Merging Path Plot: adaptive fusing of k-groups with likelihood-based model selectionCode0
Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning—0
Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece—0
MSM lag time cannot be used for variational model selection—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Evasion Attacks against Machine Learning at Test Time—0
Network Model Selection for Task-Focused Attributed Network Inference—0
Fixed effects testing in high-dimensional linear mixed models—0
When Is the First Spurious Variable Selected by Sequential Regression Procedures?—0
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