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 31–40 of 2050 papers

TitleStatusHype
Towards more transferable adversarial attack in black-box manner—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education—0
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models—0
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modelingCode0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Second-Order Convergence in Private Stochastic Non-Convex Optimization—0
LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously ThoughtCode0
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
Show:102550
← PrevPage 4 of 205Next →

No leaderboard results yet.