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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 1651–1675 of 2050 papers

TitleStatusHype
Convex Covariate Clustering for ClassificationCode0
Automatic Gradient BoostingCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
Unifying Summary Statistic Selection for Approximate Bayesian ComputationCode0
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Minimum discrepancy principle strategy for choosing k in k-NN regressionCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
Catastrophic forgetting: still a problem for DNNsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
We Need to Talk About train-dev-test SplitsCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss FunctionsCode0
Sacrificing information for the greater good: how to select photometric bands for optimal accuracyCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Capability Instruction Tuning: A New Paradigm for Dynamic LLM RoutingCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language ModelsCode0
Extremely Greedy Equivalence SearchCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
TopicNet: Making Additive Regularisation for Topic Modelling AccessibleCode0
Structured model selection via _1-_2 optimizationCode0
MLP-KAN: Unifying Deep Representation and Function LearningCode0
Face Spoofing Detection using Deep LearningCode0
Context tree selection for functional dataCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
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