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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 726–750 of 2050 papers

TitleStatusHype
Dynamic Interpretability for Model Comparison via Decision RulesCode0
Structural Risk Minimization for Learning Nonlinear Dynamics—0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
Pseudo Label Selection is a Decision Problem—0
Forecasting large collections of time series: feature-based methods—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports—0
A Convex Framework for Confounding Robust InferenceCode0
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics—0
The Topology and Geometry of Neural RepresentationsCode0
Error Reduction from Stacked Regressions—0
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy ClassificationCode0
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message LengthCode0
A novel algebraic approach to time-reversible evolutionary modelsCode0
Parameter identifiability and model selection for partial differential equation models of cell invasionCode0
Generalized Information Criteria for Structured Sparse Models—0
AutoML-GPT: Large Language Model for AutoML—0
Subjectivity in Unsupervised Machine Learning Model Selection—0
SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks—0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
Sparse Models for Machine Learning—0
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
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