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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 16011625 of 2050 papers

TitleStatusHype
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion0
On the Use of Minimum Penalties in Statistical Learning0
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring0
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law0
On uncertainty-penalized Bayesian information criterion0
On U-processes and clustering performance0
OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?0
Open, Closed, or Small Language Models for Text Classification?0
Open Problem: Model Selection for Contextual Bandits0
OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment0
OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis0
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation0
A Characterization for Optimal Bundling of Products with Non-Additive Values0
Optimal interval clustering: Application to Bregman clustering and statistical mixture learning0
Optimality in importance sampling: a gentle survey0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Robust Lasso-Zero for sparse corruption and model selection with missing covariatesCode0
Optimized Conformal Selection: Powerful Selective Inference After Conformity Score OptimizationCode0
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video AnalyticsCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender SystemsCode0
EPP: interpretable score of model predictive powerCode0
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