# Boosted Generative Models

arXiv: https://arxiv.org/abs/1702.08484 · HTML: https://mlsanity.com/paper/1702.08484/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2017.

## Built on by (most cited first, 18 of 18)

- [Tractable Control for Autoregressive Language Generation](https://mlsanity.com/paper/2304.07438/) (2023)
- [Synthetic data, real errors: how (not) to publish and use synthetic data](https://mlsanity.com/paper/2305.09235/) (2023)
- [Compositional Sculpting of Iterative Generative Processes](https://mlsanity.com/paper/2309.16115/) (2023)
- [Image Inpainting via Tractable Steering of Diffusion Models](https://mlsanity.com/paper/2401.03349/) (2023)
- [Breaking the Factorization Barrier in Diffusion Language Models](https://mlsanity.com/paper/2603.00045/) (2026)
- [Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows](https://mlsanity.com/paper/2305.18910/) (2023)
- ["Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood](https://mlsanity.com/paper/2405.16730/) (2024)
- [Ensembles of GANs for synthetic training data generation](https://mlsanity.com/paper/2104.11797/) (2021)
- [Adversarial Imitation Learning via Boosting](https://mlsanity.com/paper/2404.08513/) (2024)
- [Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey](https://mlsanity.com/paper/2111.13282/) (2021)
- [Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning](https://mlsanity.com/paper/2204.03597/) (2022)
- [Classification Diffusion Models: Revitalizing Density Ratio Estimation](https://mlsanity.com/paper/2402.10095/) (2024)
- [Binary Losses for Density Ratio Estimation](https://mlsanity.com/paper/2407.01371/) (2024)
- [Leveraging Robust Optimization for LLM Alignment under Distribution Shifts](https://mlsanity.com/paper/2504.05831/) (2025)
- [Why GANs are overkill for NLP](https://mlsanity.com/paper/2205.09838/) (2022)
- [NRGBoost: Energy-Based Generative Boosted Trees](https://mlsanity.com/paper/2410.03535/) (2024)
- [Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation](https://mlsanity.com/paper/2503.24258/) (2025)
- [PENEX: AdaBoost-Inspired Neural Network Regularization](https://mlsanity.com/paper/2510.02107/) (2025)

## Abstract (arXiv)

We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Further, our approach allows the ensemble to include discriminative models trained to distinguish real data from model-generated data. We show theoretical conditions under which incorporating a new model in the ensemble will improve the fit and empirically demonstrate the effectiveness of our black-box boosting algorithms on density estimation, classification, and sample generation on benchmark datasets for a wide range of generative models.