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 2001–2050 of 2050 papers

TitleStatusHype
Greedy metrics in orthogonal greedy learning—0
Greedy Model Averaging—0
Green AI in Action: Strategic Model Selection for Ensembles in Production—0
Green Runner: A tool for efficient deep learning component selection—0
Green Runner: A tool for efficient model selection from model repositories—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
Group-Sparse Model Selection: Hardness and Relaxations—0
Guided Recommendation for Model Fine-Tuning—0
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis—0
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts—0
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models—0
Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability—0
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers—0
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!—0
EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge—0
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Hierarchical Block Structures and High-resolution Model Selection in Large Networks—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
High-Dimensional Dynamic Covariance Models with Random Forests—0
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions—0
High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality—0
Higher-order asymptotics for the parametric complexity—0
High SNR Consistent Compressive Sensing—0
Homotopy Continuation Approaches for Robust SV Classification and Regression—0
Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
How Many Communities Are There?—0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
How to select predictive models for causal inference?—0
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets—0
Hypothesis Testing for Equality of Latent Positions in Random Graphs—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets—0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach—0
Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes—0
Identifying Technical Debt and Its Types Across Diverse Software Projects Issues—0
Impact of Loss Model Selection on Power Semiconductor Lifetime Prediction in Electric Vehicles—0
Impact of Missing Values in Machine Learning: A Comprehensive Analysis—0
Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for TurbineGovernor and Excitation System—0
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes—0
Improving Group Lasso for high-dimensional categorical data—0
Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning—0
Improving Model Robustness Using Causal Knowledge—0
Show:102550
← PrevPage 41 of 41Next →

No leaderboard results yet.