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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 1426–1450 of 2050 papers

TitleStatusHype
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Inferring Convolutional Neural Networks' accuracies from their architectural characterizationsCode0
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
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
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
Multiclass Learning from ContradictionsCode0
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
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection—0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Selective machine learning of doubly robust functionals—0
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Global Adaptive Generative Adjustment—0
Enhancing Certifiable Robustness via a Deep Model Ensemble—0
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