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 1431–1440 of 2050 papers

TitleStatusHype
Minimum Description Length Revisited—0
Misclassification bounds for PAC-Bayesian sparse deep learning—0
Mitigating covariate shift in non-colocated data with learned parameter priors—0
Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD—0
Mixture Model Averaging for Clustering—0
Mixture of neural operator experts for learning boundary conditions and model selection—0
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data—0
MLOps with enhanced performance control and observability—0
Model-Agnostic Interpretability of Machine Learning—0
Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models—0
Show:102550
← PrevPage 144 of 205Next →

No leaderboard results yet.