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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 251–275 of 2050 papers

TitleStatusHype
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Familia: An Open-Source Toolkit for Industrial Topic ModelingCode0
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
GTApprox: surrogate modeling for industrial designCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Face Spoofing Detection using Deep LearningCode0
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discoveryCode0
Extremely Greedy Equivalence SearchCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
High-dimensional classification by sparse logistic regressionCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
A survey of probabilistic generative frameworks for molecular simulationsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Algebraic Equivalence of Linear Structural Equation ModelsCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Supervised Models Can Generalize Also When Trained on Random LabelCode0
Evaluation of HTR models without Ground Truth MaterialCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
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