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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 1201–1250 of 2050 papers

TitleStatusHype
Sparse Inverse Covariance Estimation with Calibration—0
Sparse Modeling for Image and Vision Processing—0
Sparse model selection in the highly under-sampled regime—0
Sparse model selection via integral terms—0
Sparse Models for Machine Learning—0
Sparse Private LASSO Logistic Regression—0
Predictor Selection for Synthetic Controls—0
Robust Model Selection and Nearly-Proper Learning for GMMs—0
Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models—0
Sparsistent Learning of Varying-coefficient Models with Structural Changes—0
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries—0
Bayesian Spatial Predictive Synthesis—0
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010—0
Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network—0
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation—0
Speedy Model Selection (SMS) for Copula Models—0
Spike and slab variational Bayes for high dimensional logistic regression—0
Spiking Neural Networks Hardware Implementations and Challenges: a Survey—0
Split LBI: An Iterative Regularization Path with Structural Sparsity—0
Stabilizing black-box model selection with the inflated argmax—0
Stationary Geometric Graphical Model Selection—0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Statistical Inference for Pairwise Graphical Models Using Score Matching—0
Statistical inference for quantum singular models—0
Statistical inference of assortative community structures—0
Statistical Model Criticism of Variational Auto-Encoders—0
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems—0
Structural Learning of Multivariate Regression Chain Graphs via Decomposition—0
Structural Risk Minimization for Learning Nonlinear Dynamics—0
Structure Learning in Gaussian Graphical Models from Glauber Dynamics—0
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control—0
Student-t Processes as Alternatives to Gaussian Processes—0
Subjectivity in Unsupervised Machine Learning Model Selection—0
Subsampling Graphs with GNN Performance Guarantees—0
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data—0
Superpixel-guided Two-view Deterministic Geometric Model Fitting—0
Supervised structure learning—0
Straight-Through meets Sparse Recovery: the Support Exploration Algorithm—0
Surfing the modeling of PoS taggers in low-resource scenarios—0
Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics—0
Synthetic Data for Model Selection—0
Systematic Ensemble Model Selection Approach for Educational Data Mining—0
Understanding Best Subset Selection: A Tale of Two C(omplex)ities—0
Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernel—0
Target Variable Engineering—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Techniques for clustering interaction data as a collection of graphs—0
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work—0
Temporal Answer Set Programming—0
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