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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
Dimensionality Dependent PAC-Bayes Margin Bound—0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
Bayesian taut splines for estimating the number of modes—0
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
Eagle: Efficient Training-Free Router for Multi-LLM Inference—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts—0
Bayesian leave-one-out cross-validation for large data—0
Bayesian Learning with Wasserstein Barycenters—0
Efficient Cross-Validation for Semi-Supervised Learning—0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
Efficient Distributed Estimation of Inverse Covariance Matrices—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
Adaptive Sequential Machine Learning—0
Efficient Estimation of the number of neighbours in Probabilistic K Nearest Neighbour Classification—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
Efficient Model Compression for Bayesian Neural Networks—0
Achieving Fairness with a Simple Ridge Penalty—0
Entropy-based Characterization of Modeling Constraints—0
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