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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 1001–1050 of 2050 papers

TitleStatusHype
Parameter Reference Loss for Unsupervised Domain Adaptation—0
Parameter Selection Algorithm For Continuous Variables—0
Pareto Optimal Model Selection in Linear Bandits—0
Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency—0
Parsimonious Shifted Asymmetric Laplace Mixtures—0
Partial sequence labeling with structured Gaussian Processes—0
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising—0
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression—0
Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators—0
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework—0
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
Model Validation Using Mutated Training Labels: An Exploratory Study—0
Pessimistic Model Selection for Offline Deep Reinforcement Learning—0
Physics-Aware Initialization Refinement in Code-Aided EM for Blind Channel Estimation—0
Physics-Informed Neural State Space Models via Learning and Evolution—0
Piecewise Convex Function Estimation and Model Selection—0
Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning—0
Posterior and variational inference for deep neural networks with heavy-tailed weights—0
Post-hoc Models for Performance Estimation of Machine Learning Inference—0
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models—0
Post-Selection Inference in Three-Dimensional Panel Data—0
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic—0
Practical Transferability Estimation for Image Classification Tasks—0
PreCall: A Visual Interface for Threshold Optimization in ML Model Selection—0
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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