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 1126–1150 of 2050 papers

TitleStatusHype
Robust high dimensional factor models with applications to statistical machine learning—0
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models—0
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations—0
Robust Model Selection of Gaussian Graphical Models—0
Robustness in sparse linear models: relative efficiency based on robust approximate message passing—0
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal—0
Robust Output Analysis with Monte-Carlo Methodology—0
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background—0
Robust Regression with Twinned Gaussian Processes—0
Robust Social Planning—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
Saliency Revisited: Analysis of Mouse Movements versus Fixations—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Sampling Requirements for Stable Autoregressive Estimation—0
Saturn: Efficient Multi-Large-Model Deep Learning—0
Scalable Bayesian Transformed Gaussian Processes—0
Scalable Model Selection for Belief Networks—0
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks—0
Scaling Inference-Efficient Language Models—0
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments—0
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics—0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
SCAMS: Simultaneous Clustering and Model Selection—0
Score-based Causal Learning in Additive Noise Models—0
Show:102550
← PrevPage 46 of 82Next →

No leaderboard results yet.