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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

Showing 451500 of 2050 papers

TitleStatusHype
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding SpecificitiesCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Fast Cross-Validation via Sequential TestingCode0
Odd-One-Out Representation LearningCode0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
High-Fidelity Transfer of Functional Priors for Wide Bayesian Neural Networks by Learning ActivationsCode0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Extremely Greedy Equivalence SearchCode0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Conformal online model aggregationCode0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
PARAPHRASUS : A Comprehensive Benchmark for Evaluating Paraphrase Detection ModelsCode0
Context tree selection for functional dataCode0
Pareto-optimal clustering with the primal deterministic information bottleneckCode0
Parsimonious Bayesian deep networksCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Face Spoofing Detection using Deep LearningCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Convex Covariate Clustering for ClassificationCode0
Precision-Recall-Gain Curves: PR Analysis Done RightCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Predictive Multiplicity in ClassificationCode0
Probabilistic Matrix Factorization for Automated Machine LearningCode0
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
Evaluation of HTR models without Ground Truth MaterialCode0
AALF: Almost Always Linear ForecastingCode0
Bayesian Neural Networks at Finite TemperatureCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Ranking vs. Classifying: Measuring Knowledge Base Completion QualityCode0
Automated Model Selection for Tabular DataCode0
A Realistic Protocol for Evaluation of Weakly Supervised Object LocalizationCode0
EPP: interpretable score of model predictive powerCode0
Cross-Validated Off-Policy EvaluationCode0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Cross-Validation with ConfidenceCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
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