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 1571–1580 of 2050 papers

TitleStatusHype
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
A Unified Approach to Routing and Cascading for LLMs—0
A Unified Dynamic Approach to Sparse Model Selection—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
Automated Model Selection for Generalized Linear Models—0
Automated Model Selection for Time-Series Anomaly Detection—0
Show:102550
← PrevPage 158 of 205Next →

No leaderboard results yet.