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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 301–325 of 2050 papers

TitleStatusHype
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model SelectionCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
Optimal design of experiments to identify latent behavioral typesCode0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model RankingCode0
LaPLACE: Probabilistic Local Model-Agnostic Causal ExplanationsCode0
ARDA: Automatic Relational Data Augmentation for Machine LearningCode0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
A Convex Framework for Confounding Robust InferenceCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding programCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
A principled approach to model validation in domain generalizationCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Approximate Cross-validation: Guarantees for Model Assessment and SelectionCode0
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender SystemsCode0
Anytime Model Selection in Linear BanditsCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
AnyLoss: Transforming Classification Metrics into Loss FunctionsCode0
Differentiable Model Selection for Ensemble LearningCode0
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