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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 551–575 of 2050 papers

TitleStatusHype
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
EPP: interpretable score of model predictive powerCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
Differentiable Model Selection for Ensemble LearningCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Batch Value-function Approximation with Only RealizabilityCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
Familia: An Open-Source Toolkit for Industrial Topic ModelingCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Fast Cross-Validation via Sequential TestingCode0
Deep Generalized Method of Moments for Instrumental Variable AnalysisCode0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysisCode0
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithmsCode0
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Dynamic Interpretability for Model Comparison via Decision RulesCode0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
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