# The State of the Art in Integrating Machine Learning into Visual Analytics

arXiv: https://arxiv.org/abs/1802.07954 · HTML: https://mlsanity.com/paper/1802.07954/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 24 of 24)

- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization](https://mlsanity.com/paper/2204.06504/) (2022)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions](https://mlsanity.com/paper/2205.01296/) (2022)
- [Learnable Optimal Sequential Grouping for Video Scene Detection](https://mlsanity.com/paper/2205.08249/) (2022)
- [Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research](https://mlsanity.com/paper/2308.06290/) (2023)
- [Visual Analytics for Explainable and Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2507.10240/) (2025)
- [PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback](https://mlsanity.com/paper/2104.14962/) (2021)
- [HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects of Cloud Properties on Climate Patterns](https://mlsanity.com/paper/2305.07859/) (2023)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features](https://mlsanity.com/paper/2308.00710/) (2023)
- [Is Medieval Distant Viewing Possible? : Extending and Enriching Annotation of Legacy Image Collections using Visual Analytics](https://mlsanity.com/paper/2208.09657/) (2022)
- [SWR-Viz: AI-assisted Interactive Visual Analytics Framework for Ship Weather Routing](https://mlsanity.com/paper/2511.15182/) (2025)
- [A Systematic Literature Review about Idea Mining: The Use of Machine-driven Analytics to Generate Ideas](https://mlsanity.com/paper/2202.12826/) (2022)
- [Trust Calibration as a Function of the Evolution of Uncertainty in Knowledge Generation: A Survey](https://mlsanity.com/paper/2209.04388/) (2022)
- [Challenges in anomaly and change point detection](https://mlsanity.com/paper/2212.13520/) (2022)
- [A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization](https://mlsanity.com/paper/2308.05640/) (2023)
- [Eliciting Model Steering Interactions from Users via Data and Visual Design Probes](https://mlsanity.com/paper/2310.09314/) (2023)
- [A Deep User Interface for Exploring LLaMa](https://mlsanity.com/paper/2502.20938/) (2025)
- [The Human-Data-Model Interaction Canvas for Visual Analytics](https://mlsanity.com/paper/2505.07534/) (2025)
- [SemiConLens: Visual Analytics for 2D Semiconductor Discovery](https://mlsanity.com/paper/2605.04067/) (2026)
- [Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics](https://mlsanity.com/paper/2607.00969/) (2026)
- [ImputeViz: A Visual Analytics Dashboard for Diagnosing Missing Data and Comparing Imputation Methods](https://mlsanity.com/paper/2607.08579/) (2026)

## Abstract (arXiv)

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.