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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
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work—0
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think AgainCode1
Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD—0
Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Geometric and Topological Inference for Deep Representations of Complex Networks—0
Bayesian Spatial Predictive Synthesis—0
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification TasksCode1
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows—0
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