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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 1951–2000 of 2050 papers

TitleStatusHype
Reliable ABC model choice via random forests—0
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning—0
SCAMS: Simultaneous Clustering and Model Selection—0
Learning with many experts: model selection and sparsity—0
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models—0
Model Consistency of Partly Smooth Regularizers—0
Learning from Domain Complexity—0
Active Learning for Undirected Graphical Model Selection—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Selective Factor Extraction in High Dimensions—0
Optimal interval clustering: Application to Bregman clustering and statistical mixture learning—0
Exact Post Model Selection Inference for Marginal Screening—0
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression—0
Classification with Sparse Overlapping Groups—0
Student-t Processes as Alternatives to Gaussian Processes—0
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models—0
Sequential Model-Based Ensemble Optimization—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
Markov Network Structure Learning via Ensemble-of-Forests Models—0
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning AlgorithmsCode0
Learning Adaptive Value of Information for Structured Prediction—0
Bayesian Hierarchical Community Discovery—0
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Sparse Inverse Covariance Estimation with Calibration—0
On model selection consistency of penalized M-estimators: a geometric theory—0
Score-based Causal Learning in Additive Noise Models—0
Exact post-selection inference, with application to the lasso—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Aggregation of Affine Estimators—0
Parsimonious Shifted Asymmetric Laplace Mixtures—0
Combined l_1 and greedy l_0 penalized least squares for linear model selection—0
Hierarchical Block Structures and High-resolution Model Selection in Large Networks—0
Speedy Model Selection (SMS) for Copula Models—0
Likelihood Adaptively Modified Penalties—0
Risk-consistency of cross-validation with lasso-type procedures—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models—0
Fuzzy Fibers: Uncertainty in dMRI Tractography—0
Bridging Information Criteria and Parameter Shrinkage for Model Selection—0
An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework—0
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM—0
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting—0
Segmentation et Interprétation de Nuages de Points pour la Modélisation d'Environnements Urbains—0
Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier—0
On model selection consistency of regularized M-estimators—0
Efficient Estimation of the number of neighbours in Probabilistic K Nearest Neighbour Classification—0
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition—0
A Junction Tree Framework for Undirected Graphical Model Selection—0
Group-Sparse Model Selection: Hardness and Relaxations—0
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