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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 1101–1150 of 2050 papers

TitleStatusHype
Variational Inference and Model Selection with Generalized Evidence Bounds—0
Variational Selection of Features for Molecular Kinetics—0
Vector autoregression models with skewness and heavy tails—0
Verifying Learning-Based Robotic Navigation Systems—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—0
View-graph Selection Framework for SfM—0
Virtual Reference Feedback Tuning with data-driven reference model selection—0
A User-based Visual Analytics Workflow for Exploratory Model Analysis—0
Visual Sentiment Analysis from Disaster Images in Social Media—0
Volatility Forecasting with 1-dimensional CNNs via transfer learning—0
Volumes of logistic regression models with applications to model selection—0
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis—0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System—0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
Weighted Leave-One-Out Cross Validation—0
Weighted Likelihood Policy Search with Model Selection—0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
What are the mechanisms underlying metacognitive learning?—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
When Is the First Spurious Variable Selected by Sequential Regression Procedures?—0
Which is the best model for my data?—0
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits—0
Why risk matters for protein binder design—0
Within-vector viral dynamics challenges how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya—0
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media—0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
Zero-Shot Forecasting Mortality Rates: A Global Study—0
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!—0
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets—0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions—0
Evaluating Gender Bias in Large Language Models—0
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts—0
Evaluating Meta-Regression Techniques: A Simulation Study on Heterogeneity in Location and Time—0
Evaluating Representations with Readout Model Switching—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
Evaluating the Utility of Model Explanations for Model Development—0
Evaluating Word Embeddings on Low-Resource Languages—0
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage—0
e-Values for Real-Time Residential Electricity Demand Forecast Model Selection—0
Evasion Attacks against Machine Learning at Test Time—0
Event Data Association via Robust Model Fitting for Event-based Object Tracking—0
Exact Dimensionality Selection for Bayesian PCA—0
Exact Post Model Selection Inference for Marginal Screening—0
Exact post-selection inference, with application to the lasso—0
Experiment Planning with Function Approximation—0
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