# User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

arXiv: https://arxiv.org/abs/1710.04881 · HTML: https://mlsanity.com/paper/1710.04881/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design](https://mlsanity.com/paper/2303.02884/) (2023)
- [Approximate Bayesian Computation with Domain Expert in the Loop](https://mlsanity.com/paper/2201.12090/) (2022)
- [Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition](https://mlsanity.com/paper/2505.06325/) (2025)
- [Differentiable User Models](https://mlsanity.com/paper/2211.16277/) (2022)

## Abstract (arXiv)

In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facilitate this human--machine interaction; however, fewer studies have addressed the potential defects the designs can cause. Effective interaction often requires exposing the user to the training data or its statistics. The design of the system is then critical, as this can lead to double use of data and overfitting, if the user reinforces noisy patterns in the data. We propose a user modelling methodology, by assuming simple rational behaviour, to correct the problem. We show, in a user study with 48 participants, that the method improves predictive performance in a sparse linear regression sentiment analysis task, where graded user knowledge on feature relevance is elicited. We believe that the key idea of inferring user knowledge with probabilistic user models has general applicability in guarding against overfitting and improving interactive machine learning.