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 1561–1570 of 2050 papers

TitleStatusHype
On Leakage of Code Generation Evaluation Datasets—0
On Learning to Prove—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data—0
Online Foundation Model Selection in Robotics—0
Online Laplace Model Selection Revisited—0
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks—0
Online Learning with Predictable Sequences—0
Online Learning with Regularized Kernel for One-class Classification—0
Online Model Selection: a Rested Bandit Formulation—0
Show:102550
← PrevPage 157 of 205Next →

No leaderboard results yet.