# GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries

arXiv: https://arxiv.org/abs/2411.03936 · HTML: https://mlsanity.com/paper/2411.03936/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Kernel Mean Embedding of Distributions: A Review and Beyond](https://mlsanity.com/paper/1605.09522/) (2016)
- [Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets](https://mlsanity.com/paper/2205.07249/) (2022)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [Structured Uncertainty Prediction Networks](https://mlsanity.com/paper/1802.07079/) (2018)
- [Explicitly Minimizing the Blur Error of Variational Autoencoders](https://mlsanity.com/paper/2304.05939/) (2023)
- [Generating Contextual Load Profiles Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2209.04056/) (2022)
- [Structured Uncertainty in the Observation Space of Variational Autoencoders](https://mlsanity.com/paper/2205.12533/) (2022)
- [PaDPaF: Partial Disentanglement with Partially-Federated GANs](https://mlsanity.com/paper/2212.03836/) (2022)
- [Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation](https://mlsanity.com/paper/2204.10770/) (2022)
- [Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data](https://mlsanity.com/paper/2407.11785/) (2024)

## Built on by (most cited first, 3 of 3)

- [One Model to Forecast Them All and in Entity Distributions Bind Them](https://mlsanity.com/paper/2501.15499/) (2025)
- [Clustering Rooftop PV Systems via Probabilistic Embeddings](https://mlsanity.com/paper/2505.10699/) (2025)
- [The Living Forecast: Evolving Day-Ahead Predictions into Intraday Reality](https://mlsanity.com/paper/2510.12271/) (2025)

## Abstract (arXiv)

Generative modelling of multi-user datasets has become prominent in science and engineering. Generating a data point for a given user requires employing user information, and conventional generative models, including variational autoencoders (VAEs), often ignore this. This paper introduces GUIDE-VAE, a novel conditional generative model that leverages user embeddings to generate user-guided data. By leveraging shared patterns across users, GUIDE-VAE improves performance in multi-user settings, even under significant data imbalance. In addition to integrating user information, GUIDE-VAE incorporates a pattern dictionary-based covariance composition (PDCC) to improve the realism of generated samples by capturing complex feature dependencies. While user embeddings drive performance gains, PDCC addresses common issues such as noise and over-smoothing typically seen in VAEs. The proposed GUIDE-VAE was evaluated on a multi-user smart meter dataset characterised by substantial data imbalance across users. Quantitative results show that GUIDE-VAE performs effectively on both synthetic data generation and missing-record imputation tasks, while qualitative evaluations indicate that it produces more plausible and less noisy data. These results establish GUIDE-VAE as a promising tool for controlled, realistic data generation in multi-user datasets, with potential applications across domains that require user-informed modelling.