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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 626–650 of 2050 papers

TitleStatusHype
Decomposing Gaussians with Unknown CovarianceCode0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
EPP: interpretable score of model predictive powerCode0
Entity Set Search of Scientific Literature: An Unsupervised Ranking ApproachCode0
Dynamic Interpretability for Model Comparison via Decision RulesCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Differentiable Model Selection for Ensemble LearningCode0
Automatic Gradient BoostingCode0
Machine learning for sports betting: should model selection be based on accuracy or calibration?Code0
Machine learning in policy evaluation: new tools for causal inferenceCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian OptimizationCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
MGTCOM: Community Detection in Multimodal GraphsCode0
Precision-Recall-Gain Curves: PR Analysis Done RightCode0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
Data-Driven Online Model Selection With Regret Guarantees—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models—0
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