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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 1126–1150 of 2050 papers

TitleStatusHype
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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