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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 1711–1720 of 2050 papers

TitleStatusHype
Finding the Homology of Decision Boundaries with Active LearningCode0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count DataCode0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained ModelsCode0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants. Application to wastewater samples (ANRS0160)Code0
Patched RTC: evaluating LLMs for diverse software development tasksCode0
Flexible, Non-parametric Modeling Using Regularized Neural NetworksCode0
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