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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 351–375 of 2050 papers

TitleStatusHype
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Greedy equivalence search for nonparametric graphical models—0
MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic CommunicationCode0
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital TwinningCode0
Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis—0
Exploring Design Choices for Building Language-Specific LLMsCode0
aeon: a Python toolkit for learning from time seriesCode5
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU TasksCode2
Statistical Uncertainty in Word Embeddings: GloVe-VCode1
MSBoost: Using Model Selection with Multiple Base Estimators for Gradient BoostingCode0
Increasing certainty in systems biology models using Bayesian multimodel inferenceCode0
Efficient Sequential Decision Making with Large Language Models—0
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
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
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
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
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