# Recent Research Advances on Interactive Machine Learning

arXiv: https://arxiv.org/abs/1811.04548 · HTML: https://mlsanity.com/paper/1811.04548/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 17 of 17)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021)
- [Interactive Dimensionality Reduction for Comparative Analysis](https://mlsanity.com/paper/2106.15481/) (2021)
- [Towards Visual Explainable Active Learning for Zero-Shot Classification](https://mlsanity.com/paper/2108.06730/) (2021)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring](https://mlsanity.com/paper/2111.10010/) (2021)
- [Practitioner Motives to Use Different Hyperparameter Optimization Methods](https://mlsanity.com/paper/2203.01717/) (2022)
- [Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework](https://mlsanity.com/paper/2302.07187/) (2023)
- [Multimedia and Visual Analytics in the Agentic Era](https://mlsanity.com/paper/2504.06138/) (2025)
- [Reconnoitering the class distinguishing abilities of the features, to know them better](https://mlsanity.com/paper/2211.12771/) (2022)
- [Eliciting Model Steering Interactions from Users via Data and Visual Design Probes](https://mlsanity.com/paper/2310.09314/) (2023)
- [Human-in-the-Loop Segmentation of Multi-species Coral Imagery](https://mlsanity.com/paper/2404.09406/) (2024)
- ["Stop replacing salt with sugar!'': Towards Intuitive Human-Agent Teaching](https://mlsanity.com/paper/2509.24651/) (2025)

## Abstract (arXiv)

Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.