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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

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Showing 851–900 of 2050 papers

TitleStatusHype
A Strong Baseline for Batch Imitation Learning—0
Determination of Latent Dimensionality in International Trade Flow—0
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Graphical LASSO Based Model Selection for Time Series—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Graph Similarity Description: How Are These Graphs Similar?—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks—0
Greedy equivalence search for nonparametric graphical models—0
Greedy metrics in orthogonal greedy learning—0
Greedy Model Averaging—0
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables—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
A Bayesian Model for Bivariate Causal Inference—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
Communication-efficient Distributed Sparse Linear Discriminant Analysis—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
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—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
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—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
A Survey on Theoretical Advances of Community Detection in 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
Comparing Bayesian Models of Annotation—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
Detection of intensity bursts using Hawkes processes: an application to high frequency financial data—0
Higher-order asymptotics for the parametric complexity—0
Detection and Evaluation of Clusters within Sequential Data—0
High SNR Consistent Compressive Sensing—0
Bayesian CART models for insurance claims frequency—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—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
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
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