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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
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
Machine learning in policy evaluation: new tools for causal inferenceCode0
On the complexity of logistic regression models—0
Unsupervised Attention Mechanism across Neural Network LayersCode0
Representation Learning with Weighted Inner Product for Universal Approximation of General SimilaritiesCode0
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning—0
Automated Model Selection with Bayesian Quadrature—0
Logistic principal component analysis via non-convex singular value thresholding—0
Deep Bayesian Multi-Target Learning for Recommender SystemsCode0
Anomaly Detection for an E-commerce Pricing System—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
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Separating common (global and local) and distinct variation in multiple mixed types data setsCode0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Efficient Cross-Validation for Semi-Supervised Learning—0
Differential Description Length for Hyperparameter Selection in Machine 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
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding SpecificitiesCode0
Learning for Multi-Model and Multi-Type Fitting—0
Testing Conditional Independence in Supervised Learning AlgorithmsCode1
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
Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for TurbineGovernor and Excitation System—0
Automated Adaptation Strategies for Stream LearningCode0
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
Physics-Based Learning for Robotic Environmental Sensing—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection—0
Learning Vine Copula Models For Synthetic Data Generation—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
Variational Selection of Features for Molecular Kinetics—0
LM-BIC Model Selection in Semiparametric Models—0
Model Evaluation, Model Selection, and Algorithm Selection in Machine LearningCode0
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