# Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

arXiv: https://arxiv.org/abs/1705.09862 · HTML: https://mlsanity.com/paper/1705.09862/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Bayesian Optimization with High-Dimensional Outputs](https://mlsanity.com/paper/2106.12997/) (2021)
- [Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems](https://mlsanity.com/paper/2106.04221/) (2021)
- [Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets](https://mlsanity.com/paper/2308.16822/) (2023)
- [Model Selection for Production System via Automated Online Experiments](https://mlsanity.com/paper/2105.13420/) (2021)
- [Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference](https://mlsanity.com/paper/2407.02476/) (2024)
- [Zero-Shot Performance Prediction for Probabilistic Scaling Laws](https://mlsanity.com/paper/2510.16743/) (2025)
- [Aligned Multi-Task Gaussian Process](https://mlsanity.com/paper/2110.15761/) (2021)
- [Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes](https://mlsanity.com/paper/2207.06544/) (2022)
- [Weighted-Sum of Gaussian Process Latent Variable Models](https://mlsanity.com/paper/2402.09122/) (2024)
- [Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems](https://mlsanity.com/paper/2606.06576/) (2026)
- [A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation](https://mlsanity.com/paper/2312.10023/) (2023)
- [Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays](https://mlsanity.com/paper/2402.17704/) (2024)
- [Improving Forecasts of Suicide Attempts for Patients with Little Data](https://mlsanity.com/paper/2511.18199/) (2025)
- [Transformed Latent Variable Multi-Output Gaussian Processes](https://mlsanity.com/paper/2605.05133/) (2026)

## Abstract (arXiv)

Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a model that captures the latent information related to these conditions and generalize to a new one with few data? We present a new model called Latent Variable Multiple Output Gaussian Processes (LVMOGP) and that allows to jointly model multiple conditions for regression and generalize to a new condition with a few data points at test time. LVMOGP infers the posteriors of Gaussian processes together with a latent space representing the information about different conditions. We derive an efficient variational inference method for LVMOGP, of which the computational complexity is as low as sparse Gaussian processes. We show that LVMOGP significantly outperforms related Gaussian process methods on various tasks with both synthetic and real data.