SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1210 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
Show:102550
← PrevPage 121 of 205Next →

No leaderboard results yet.