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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 631–640 of 2050 papers

TitleStatusHype
Green Runner: A tool for efficient deep learning component selection—0
Is K-fold cross validation the best model selection method for Machine Learning?—0
MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property RetrievalCode0
Fast Partition-Based Cross-Validation With Centering and Scaling for X^TX and X^TY—0
Towards Improved Variational Inference for Deep Bayesian Models—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
An Axiomatic Approach to Model-Agnostic Concept Explanations—0
Experiment Planning with Function Approximation—0
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