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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 1751–1800 of 2050 papers

TitleStatusHype
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell TimeCode0
High-dimensional classification by sparse logistic regressionCode0
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation—0
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical ModelsCode0
Awkt: A Physicochemical Parameter Estimation Tool for Capillary Zone Electrophoresis—0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
Saliency Revisited: Analysis of Mouse Movements versus Fixations—0
Model Selection in Bayesian Neural Networks via Horseshoe PriorsCode0
Bayesian stochastic blockmodeling—0
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders—0
Consistencies and inconsistencies between model selection and link prediction in networks—0
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction—0
( β, )-stability for cross-validation and the choice of the number of folds—0
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm—0
Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network—0
Probabilistic Matrix Factorization for Automated Machine LearningCode0
Automatic Response Category Combination in Multinomial Logistic Regression—0
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors—0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes—0
Dynamic Model Selection for Prediction Under a Budget—0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
On Bayesian Exponentially Embedded Family for Model Order Selection—0
Bandit-Based Model Selection for Deformable Object Manipulation—0
Cross-Validation with ConfidenceCode0
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms—0
High SNR Consistent Compressive Sensing—0
Exact Dimensionality Selection for Bayesian PCA—0
The variational Laplace approach to approximate Bayesian inference—0
Modeling non-stationarities in high-frequency financial time series—0
Objective Bayesian Analysis for Change Point Problems—0
Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry DataCode0
metboost: Exploratory regression analysis with hierarchically clustered dataCode0
Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models—0
Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution—0
Parameter Selection Algorithm For Continuous Variables—0
On the Sample Complexity of Graphical Model Selection for Non-Stationary ProcessesCode0
Online Learning with Regularized Kernel for One-class Classification—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Optimal statistical decision for Gaussian graphical model selection—0
Bayesian model selection consistency and oracle inequality with intractable marginal likelihood—0
Network cross-validation by edge sampling—0
Clipper: A Low-Latency Online Prediction Serving System—0
Boosting for Efficient Model Selection for Syntactic Parsing—0
PAG2ADMG: An Algorithm for the Complete Causal Enumeration of a Markov Equivalence Class—0
Split LBI: An Iterative Regularization Path with Structural Sparsity—0
Statistical Inference for Pairwise Graphical Models Using Score Matching—0
Bayesian optimization for automated model selection—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
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