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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 1551–1600 of 2050 papers

TitleStatusHype
Variational Bayes for high-dimensional linear regression with sparse priors—0
Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation—0
Deep Learning Inversion of Electrical Resistivity Data—0
Bayesian Neural Networks at Finite TemperatureCode0
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph ClusteringCode0
Adaptive Sequential Machine Learning—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection—0
Combining Model and Parameter Uncertainty in Bayesian Neural NetworksCode0
On the Computation and Applications of Large Dense Partial Correlation Networks—0
Regularity Normalization: Constraining Implicit Space with Minimum Description Length—0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
Convex Covariate Clustering for ClassificationCode0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
On the complexity of logistic regression models—0
Machine learning in policy evaluation: new tools for causal inferenceCode0
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning—0
Representation Learning with Weighted Inner Product for Universal Approximation of General SimilaritiesCode0
Unsupervised Attention Mechanism across Neural Network LayersCode0
Automated Model Selection with Bayesian Quadrature—0
Deep Bayesian Multi-Target Learning for Recommender SystemsCode0
Anomaly Detection for an E-commerce Pricing System—0
Logistic principal component analysis via non-convex singular value thresholding—0
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributionsCode0
Bayesian Anomaly Detection and Classification—0
An information criterion for auxiliary variable selection in incomplete data analysis—0
Separating common (global and local) and distinct variation in multiple mixed types data setsCode0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Differential Description Length for Hyperparameter Selection in Machine Learning—0
Efficient Cross-Validation for Semi-Supervised Learning—0
Model Selection for Simulator-based Statistical Models: A Kernel Approach—0
Un modèle Bayésien de co-clustering de données mixtes—0
Learning Counterfactual Representations for Estimating Individual Dose-Response CurvesCode0
Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models—0
Learning for Multi-Model and Multi-Type Fitting—0
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding SpecificitiesCode0
Clustering Discrete-Valued Time Series—0
On the cross-validation bias due to unsupervised pre-processingCode0
Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship—0
lassopack: Model selection and prediction with regularized regression in Stata—0
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—0
Automated Adaptation Strategies for Stream LearningCode0
Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for TurbineGovernor and Excitation System—0
A Bayesian Model for Bivariate Causal Inference—0
Learning Representations from Dendrograms—0
Breaking the bonds of weak coupling: the dynamic causal modelling of oscillator amplitudes—0
Recurrent Neural Networks for Fuzz Testing Web BrowsersCode0
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