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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 1651–1700 of 2050 papers

TitleStatusHype
An Approach to the CLPsych 2018 Shared Task Using Top-Down Text Representation and Simple Bottom-Up Model Selection—0
Bayesian Learning with Wasserstein Barycenters—0
A Local Information Criterion for Dynamical Systems—0
Topological Data Analysis of Decision Boundaries with Application to Model SelectionCode0
Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing—0
Best of many worlds: Robust model selection for online supervised learning—0
Parsimonious Bayesian deep networksCode0
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
Bayesian Joint Spike-and-Slab Graphical LassoCode0
Analyzing order flows in limit order books with ratios of Cox-type intensities—0
Model selection with lasso-zero: adding straw to the haystack to better find needlesCode0
Spatio-temporal Bayesian On-line Changepoint Detection with Model SelectionCode0
TensOrMachine: Probabilistic Boolean Tensor DecompositionCode0
Superpixel-guided Two-view Deterministic Geometric Model Fitting—0
Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection—0
Entity Set Search of Scientific Literature: An Unsupervised Ranking ApproachCode0
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach—0
Expert Finding in Community Question Answering: A Review—0
Multi-locus data distinguishes between population growth and multiple merger coalescentsCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
Binary Matrix Factorization via Dictionary Learning—0
A comparison of methods for model selection when estimating individual treatment effectsCode1
A Latent Gaussian Mixture Model for Clustering Longitudinal Data—0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
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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