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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 376–400 of 2050 papers

TitleStatusHype
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
EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge—0
The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity—0
The Evolution of Multimodal Model Architectures—0
The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model—0
Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmarkCode0
Cross-Validated Off-Policy EvaluationCode0
AnyLoss: Transforming Classification Metrics into Loss FunctionsCode0
Symmetric Linear Bandits with Hidden SymmetryCode0
Green AI in Action: Strategic Model Selection for Ensembles in Production—0
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
Movie Revenue Prediction using Machine Learning ModelsCode1
Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models—0
Comparative Analysis of Predicting Subsequent Steps in Hénon Map—0
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations—0
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking—0
Don't Waste Your Time: Early Stopping Cross-ValidationCode0
On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models—0
Misclassification bounds for PAC-Bayesian sparse deep learning—0
KITE: A Kernel-based Improved Transferability Estimation Method—0
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System—0
mlr3summary: Concise and interpretable summaries for machine learning modelsCode0
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