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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 361–370 of 2050 papers

TitleStatusHype
Efficient Sequential Decision Making with Large Language Models—0
Increasing certainty in systems biology models using Bayesian multimodel inferenceCode0
Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways—0
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy ModelsCode0
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models—0
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection—0
Design and Scheduling of an AI-based Queueing System—0
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer VisionCode0
Posterior and variational inference for deep neural networks with heavy-tailed weights—0
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN PerformanceCode0
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