# The Human Kernel

arXiv: https://arxiv.org/abs/1510.07389 · HTML: https://mlsanity.com/paper/1510.07389/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2015.

## Built on by (most cited first, 11 of 11)

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Bayesian Model Selection, the Marginal Likelihood, and Generalization](https://mlsanity.com/paper/2202.11678/) (2022)
- [HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes](https://mlsanity.com/paper/2212.10538/) (2022)
- [Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations](https://mlsanity.com/paper/2206.11574/) (2022)
- [A Self-Supervised Framework for Function Learning and Extrapolation](https://mlsanity.com/paper/2106.07369/) (2021)
- [Bandits for Learning to Explain from Explanations](https://mlsanity.com/paper/2102.03815/) (2021)
- [Uncertainty quantification and exploration-exploitation trade-off in humans](https://mlsanity.com/paper/2102.07647/) (2021)
- [Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task](https://mlsanity.com/paper/2104.09237/) (2021)
- [Gamifying optimization: a Wasserstein distance-based analysis of human search](https://mlsanity.com/paper/2112.06292/) (2021)
- [Beyond Transformers for Function Learning](https://mlsanity.com/paper/2304.09979/) (2023)
- [In-Context Function Learning in Large Language Models](https://mlsanity.com/paper/2602.11863/) (2026)

## Abstract (arXiv)

Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity. However, automating human expertise remains elusive; for example, Gaussian processes with standard kernels struggle on function extrapolation problems that are trivial for human learners. In this paper, we create function extrapolation problems and acquire human responses, and then design a kernel learning framework to reverse engineer the inductive biases of human learners across a set of behavioral experiments. We use the learned kernels to gain psychological insights and to extrapolate in human-like ways that go beyond traditional stationary and polynomial kernels. Finally, we investigate Occam's razor in human and Gaussian process based function learning.