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 1701–1710 of 2050 papers

TitleStatusHype
Parameter-free online learning via model selection—0
Debiased Machine Learning of Set-Identified Linear Models—0
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence—0
The information bottleneck and geometric clusteringCode0
Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models—0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
Learning Sparse Neural Networks through L_0 RegularizationCode0
Episodic memory for continual model learning—0
Scalable Model Selection for Belief Networks—0
Regularized Modal Regression with Applications in Cognitive Impairment Prediction—0
Show:102550
← PrevPage 171 of 205Next →

No leaderboard results yet.