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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 1401–1425 of 2050 papers

TitleStatusHype
Marked point processes and intensity ratios for limit order book modeling—0
The Reciprocal Bayesian LASSOCode0
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection—0
Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions—0
Evaluating Weakly Supervised Object Localization Methods RightCode1
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Inferring Convolutional Neural Networks' accuracies from their architectural characterizationsCode0
Source Model Selection for Deep Learning in the Time Series DomainCode1
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
On hyperparameter tuning in general clustering problemsm—0
Meta-Learning PAC-Bayes Priors in Model Averaging—0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Bayesian high-dimensional linear regression with generic spike-and-slab priors—0
Learning high-dimensional probability distributions using tree tensor networks—0
Noise Fit, Estimation Error and a Sharpe Information Criterion—0
Forward and Backward Feature Selection for Query Performance Prediction—0
Bayesian Model Selection for Change Point Detection and Clustering—0
Automated Dependence PlotsCode0
Multiclass Learning from ContradictionsCode0
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
On model selection for scalable time series forecasting in transport networks—0
Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza DataCode0
Improving Model Robustness Using Causal Knowledge—0
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