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 711–720 of 2050 papers

TitleStatusHype
Conformal Prediction with Upper and Lower Bound Models—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts—0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data—0
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Show:102550
← PrevPage 72 of 205Next →

No leaderboard results yet.