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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 1026–1050 of 2050 papers

TitleStatusHype
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks—0
Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks—0
Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability—0
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning—0
Prediction in latent factor regression: Adaptive PCR and beyond—0
Predictive Coarse-Graining—0
Predictive Matrix-Variate t Models—0
Predictive Modeling through Hyper-Bayesian Optimization—0
Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks—0
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach—0
Predictive variational autoencoder for learning robust representations of time-series data—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Pre-Trained Model Recommendation for Downstream Fine-tuning—0
Vision-Language Model Selection and Reuse for Downstream Adaptation—0
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models—0
Principled model selection for stochastic dynamics—0
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets—0
Private Selection with Heterogeneous Sensitivities—0
Probabilistic latent variable models for distinguishing between cause and effect—0
Probabilistic models of individual and collective animal behavior—0
Probability Distribution on Full Rooted Trees—0
Probing Task-Oriented Dialogue Representation from Language Models—0
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization—0
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