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 231–240 of 2050 papers

TitleStatusHype
Machine-learning Growth at Risk—0
pared: Model selection using multi-objective optimizationCode0
Weighted Leave-One-Out Cross Validation—0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models—0
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education—0
Show:102550
← PrevPage 24 of 205Next →

No leaderboard results yet.