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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 526–550 of 2050 papers

TitleStatusHype
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital TwinningCode0
MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic CommunicationCode0
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—0
Exploring Design Choices for Building Language-Specific LLMsCode0
Increasing certainty in systems biology models using Bayesian multimodel inferenceCode0
Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways—0
Efficient Sequential Decision Making with Large Language Models—0
MSBoost: Using Model Selection with Multiple Base Estimators for Gradient BoostingCode0
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy ModelsCode0
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models—0
Design and Scheduling of an AI-based Queueing System—0
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection—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
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries—0
From Structured to Unstructured:A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs—0
Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning—0
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge—0
Clustering-Based Validation Splits for Model Selection under Domain Shift—0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity—0
The Evolution of Multimodal Model Architectures—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
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