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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 151–160 of 2050 papers

TitleStatusHype
Optimality in importance sampling: a gentle survey—0
The Value of Information in Human-AI Decision-making—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Mixture of neural operator experts for learning boundary conditions and model selection—0
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Scaling Inference-Efficient Language Models—0
Vision-Language Model Selection and Reuse for Downstream Adaptation—0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
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