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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 226–250 of 2050 papers

TitleStatusHype
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems—0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice—0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Machine-learning Growth at Risk—0
pared: Model selection using multi-objective optimizationCode0
Weighted Leave-One-Out Cross Validation—0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models—0
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Towards more transferable adversarial attack in black-box manner—0
Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education—0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modelingCode0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Second-Order Convergence in Private Stochastic Non-Convex Optimization—0
LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously ThoughtCode0
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
An Asymptotic Equation Linking WAIC and WBIC in Singular Models—0
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