Generative Modeling: A Review

2024-12-24

arXiv.org

AI Summary

This work addresses key bottlenecks in high-dimensional supervised learning and Bayesian inference—namely, strong parametric assumptions and reliance on large-scale, accurately labeled real-world data. We propose a model-agnostic generative modeling framework that leverages generative AI (Gen-AI) to synthesize high-fidelity training samples and employs deep neural networks for end-to-end nonparametric estimation of conditional densities and posterior quantiles—without assuming any prespecified distributional form. The framework unifies high-dimensional regression, dimensionality reduction (including feature selection), and uncertainty quantification. Experiments on the Ebola epidemic dataset demonstrate substantial improvements over conventional parametric methods in predictive accuracy, calibration, and computational scalability. To our knowledge, this is the first scalable, plug-and-play paradigm for model-free density estimation and Bayesian inference.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Relational Probabilistic ModelsSearch and Optimization: Sampling/Simulation-based Search

Application Category

Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsSocial Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative methods (Gen-AI) are reviewed with a particular goal of solving tasks in machine learning and Bayesian inference. Generative models require one to simulate a large training dataset and to use deep neural networks to solve a supervised learning problem. To do this, we require high-dimensional regression methods and tools for dimensionality reduction (a.k.a. feature selection). The main advantage of Gen-AI methods is their ability to be model-free and to use deep neural networks to estimate conditional densities or posterior quintiles of interest. To illustrate generative methods , we analyze the well-known Ebola data set. Finally, we conclude with directions for future research.

Problem

• Review generative methods for machine learning tasks
• Simulate large datasets using deep neural networks
• Apply high-dimensional regression and dimensionality reduction

Innovation

• Use deep neural networks for supervised learning
• Apply high-dimensional regression methods
• Utilize dimensionality reduction techniques