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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 2011–2020 of 2050 papers

TitleStatusHype
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group AnnotationCode0
On the Sample Complexity of Graphical Model Selection for Non-Stationary ProcessesCode0
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot SystemsCode0
The use of cross validation in the analysis of designed experimentsCode0
Bayesian Neural Networks at Finite TemperatureCode0
Speech Enhancement with Zero-Shot Model SelectionCode0
Revisiting Bellman Errors for Offline Model SelectionCode0
Data-driven Advice for Applying Machine Learning to Bioinformatics ProblemsCode0
A Convex Framework for Confounding Robust InferenceCode0
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