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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
Parameter-free online learning via model selection—0
Debiased Machine Learning of Set-Identified Linear Models—0
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence—0
The information bottleneck and geometric clusteringCode0
Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models—0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
Learning Sparse Neural Networks through L_0 RegularizationCode0
Episodic memory for continual model learning—0
Scalable Model Selection for Belief Networks—0
Regularized Modal Regression with Applications in Cognitive Impairment Prediction—0
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets—0
Nonparametric Independence Screening via Favored Smoothing Bandwidth—0
Parameter Reference Loss for Unsupervised Domain Adaptation—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Network Model Selection Using Task-Focused Minimum Description Length—0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
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
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