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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 1676–1700 of 2050 papers

TitleStatusHype
Large-Scale Model Selection with Misspecification—0
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI—0
Piecewise Convex Function Estimation and Model Selection—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
HybridSVD: When Collaborative Information is Not EnoughCode0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data—0
Train on Validation: Squeezing the Data Lemon—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Region Detection in Markov Random Fields: Gaussian Case—0
Neural Architecture Search with Bayesian Optimisation and Optimal TransportCode0
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility ReportCode0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
A Work Zone Simulation Model for Travel Time Prediction in a Connected Vehicle Environment—0
Towards a more efficient representation of imputation operators in TPOT—0
Context tree selection for functional dataCode0
Comparing Bayesian Models of Annotation—0
Learning Sparse Neural Networks through L_0 Regularization—0
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
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