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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 351–360 of 2050 papers

TitleStatusHype
Causal Covariate Shift Correction using Fisher information penalty—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
The Value of Information in Human-AI Decision-making—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
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
Vision-Language Model Selection and Reuse for Downstream Adaptation—0
Scaling Inference-Efficient Language Models—0
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