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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 351–400 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
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
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
Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)—0
Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning—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
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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