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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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TitleStatusHype
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Boldness-Recalibration for Binary Event Predictions—0
Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs—0
Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables—0
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity—0
Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives—0
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
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