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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 1201–1225 of 2050 papers

TitleStatusHype
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity—0
Scalable Marginal Likelihood Estimation for Model Selection in Deep LearningCode0
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Model Selection's Disparate Impact in Real-World Deep Learning Applications—0
Model Selection for Time Series Forecasting: Empirical Analysis of Different EstimatorsCode0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation—0
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
The Shape of Learning Curves: a ReviewCode0
Deep Time Series Models for Scarce Data—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Model Complexity of Deep Learning: A Survey—0
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases—0
General Bayesian time-varying parameter VARs for predicting government bond yields—0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
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