# Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach

arXiv: https://arxiv.org/abs/2207.00128 · HTML: https://mlsanity.com/paper/2207.00128/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining](https://mlsanity.com/paper/2006.09191/) (2020)
- [Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters](https://mlsanity.com/paper/1511.06727/) (2015)
- [BOCK : Bayesian Optimization with Cylindrical Kernels](https://mlsanity.com/paper/1806.01619/) (2018)
- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Improving black-box optimization in VAE latent space using decoder uncertainty](https://mlsanity.com/paper/2107.00096/) (2021)
- [Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders](https://mlsanity.com/paper/2002.07514/) (2020)
- [Good practices for Bayesian Optimization of high dimensional structured spaces](https://mlsanity.com/paper/2012.15471/) (2020)
- [Constrained Bayesian Optimization for Automatic Chemical Design](https://mlsanity.com/paper/1709.05501/) (2017)
- [AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond](https://mlsanity.com/paper/2105.07485/) (2021)
- [Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm](https://mlsanity.com/paper/2006.12703/) (2020)
- [On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation](https://mlsanity.com/paper/1909.13668/) (2019)
- [A Gradient-based Bilevel Optimization Approach for Tuning Hyperparameters in Machine Learning](https://mlsanity.com/paper/2007.11022/) (2020)
- [Robust Feature Disentanglement in Imaging Data via Joint Invariant Variational Autoencoders: from Cards to Atoms](https://mlsanity.com/paper/2104.10180/) (2021)
- [Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders](https://mlsanity.com/paper/2106.12472/) (2021)

## Built on by (most cited first, 7 of 7)

- [GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs](https://mlsanity.com/paper/2411.14133/) (2024)
- [PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance](https://mlsanity.com/paper/2312.16983/) (2023)
- [Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders](https://mlsanity.com/paper/2303.18236/) (2023)
- [Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis](https://mlsanity.com/paper/2302.04216/) (2023)
- [A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments](https://mlsanity.com/paper/2304.02484/) (2023)
- [Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure](https://mlsanity.com/paper/2601.00855/) (2025)
- [Learning Sparse Decision Trees via Transformer Variational Auto-Encoders](https://mlsanity.com/paper/2609.01430/) (2026)

## Abstract (arXiv)

Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE) have become widely adopted across multiple areas of physics, chemistry, and materials sciences due to their capability in disentangling representations and ability to find latent manifolds for classification and regression of complex experimental data. Like other ML problems, VAEs require hyperparameter tuning, e.g., balancing the Kullback Leibler (KL) and reconstruction terms. However, the training process and resulting manifold topology and connectivity depend not only on hyperparameters, but also their evolution during training. Because of the inefficiency of exhaustive search in a high-dimensional hyperparameter space for the expensive to train models, here we explored a latent Bayesian optimization (zBO) approach for the hyperparameter trajectory optimization for the unsupervised and semi-supervised ML and demonstrate for joint-VAE with rotational invariances. We demonstrate an application of this method for finding joint discrete and continuous rotationally invariant representations for MNIST and experimental data of a plasmonic nanoparticles material system. The performance of the proposed approach has been discussed extensively, where it allows for any high dimensional hyperparameter tuning or trajectory optimization of other ML models.