# Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

arXiv: https://arxiv.org/abs/1805.10407 · HTML: https://mlsanity.com/paper/1805.10407/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2018.

## Built on by (most cited first, 30 of 34)

- [USB: A Unified Semi-supervised Learning Benchmark for Classification](https://mlsanity.com/paper/2208.07204/) (2022)
- [Extending the WILDS Benchmark for Unsupervised Adaptation](https://mlsanity.com/paper/2112.05090/) (2021)
- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors](https://mlsanity.com/paper/2205.10279/) (2022)
- [NP-Match: When Neural Processes meet Semi-Supervised Learning](https://mlsanity.com/paper/2207.01066/) (2022)
- [Improved Training of Physics-Informed Neural Networks with Model Ensembles](https://mlsanity.com/paper/2204.05108/) (2022)
- [FisherMatch: Semi-Supervised Rotation Regression via Entropy-based Filtering](https://mlsanity.com/paper/2203.15765/) (2022)
- [Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks](https://mlsanity.com/paper/2302.07579/) (2023)
- [Semi-Supervised Graph Imbalanced Regression](https://mlsanity.com/paper/2305.12087/) (2023)
- [MMD-B-Fair: Learning Fair Representations with Statistical Testing](https://mlsanity.com/paper/2211.07907/) (2022)
- [PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance](https://mlsanity.com/paper/2312.16983/) (2023)
- [Twin Neural Network Regression is a Semi-Supervised Regression Algorithm](https://mlsanity.com/paper/2106.06124/) (2021)
- [High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling](https://mlsanity.com/paper/2305.02614/) (2023)
- [Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting](https://mlsanity.com/paper/2503.17984/) (2025)
- [Semi-supervised Learning for Data-driven Soft-sensing of Biological and Chemical Processes](https://mlsanity.com/paper/2107.13822/) (2021)
- [Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation](https://mlsanity.com/paper/2204.08271/) (2022)
- [A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features](https://mlsanity.com/paper/2105.00967/) (2021)
- [Learning Representations for Independence Testing](https://mlsanity.com/paper/2409.06890/) (2024)
- [Natural Evolutionary Search meets Probabilistic Numerics](https://mlsanity.com/paper/2507.07288/) (2025)
- [Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis](https://mlsanity.com/paper/2209.10425/) (2022)
- [NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning](https://mlsanity.com/paper/2301.13569/) (2023)
- [Mixed Semi-Supervised Generalized-Linear-Regression with Applications to Deep-Learning and Interpolators](https://mlsanity.com/paper/2302.09526/) (2023)
- [Built Year Prediction from Buddha Face with Heterogeneous Labels](https://mlsanity.com/paper/2109.00812/) (2021)
- [A Generalized Weighted Optimization Method for Computational Learning and Inversion](https://mlsanity.com/paper/2201.09223/) (2022)
- [Deep Low-Density Separation for Semi-Supervised Classification](https://mlsanity.com/paper/2205.11995/) (2022)
- [Regression with Sensor Data Containing Incomplete Observations](https://mlsanity.com/paper/2304.13415/) (2023)
- [An information-Theoretic Approach to Semi-supervised Transfer Learning](https://mlsanity.com/paper/2306.06731/) (2023)
- [Exploring Probabilistic Models for Semi-supervised Learning](https://mlsanity.com/paper/2404.04199/) (2024)
- [Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation](https://mlsanity.com/paper/2508.14082/) (2025)
- [Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS](https://mlsanity.com/paper/2508.20588/) (2025)

## Abstract (arXiv)

Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression.