# A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

arXiv: https://arxiv.org/abs/2012.00467 · HTML: https://mlsanity.com/paper/2012.00467/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 28 of 28)

- [GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations](https://mlsanity.com/paper/2202.08956/) (2022)
- [DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization](https://mlsanity.com/paper/2204.06504/) (2022)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules](https://mlsanity.com/paper/2304.02173/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation](https://mlsanity.com/paper/2404.12056/) (2024)
- [DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning](https://mlsanity.com/paper/2508.01700/) (2025)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [DeepVATS: Deep Visual Analytics for Time Series](https://mlsanity.com/paper/2302.03858/) (2023)
- [Automatic Data Visualization Generation from Chinese Natural Language Questions](https://mlsanity.com/paper/2309.07650/) (2023)
- [Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles](https://mlsanity.com/paper/2207.07260/) (2022)
- [AutoDOViz: Human-Centered Automation for Decision Optimization](https://mlsanity.com/paper/2302.09688/) (2023)
- [Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancement](https://mlsanity.com/paper/2508.12555/) (2025)
- [Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement](https://mlsanity.com/paper/2303.01388/) (2023)
- [Explainable Interface for Human-Autonomy Teaming: A Survey](https://mlsanity.com/paper/2405.02583/) (2024)
- [Adversarial Attacks on Machine Learning-Aided Visualizations](https://mlsanity.com/paper/2409.02485/) (2024)
- [LLM-Assisted Visual Analytics: Opportunities and Challenges](https://mlsanity.com/paper/2409.02691/) (2024)
- [MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model](https://mlsanity.com/paper/2501.01014/) (2025)
- [Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization](https://mlsanity.com/paper/2503.00228/) (2025)
- [Evaluating Graphical Perception Capabilities of Vision Transformers](https://mlsanity.com/paper/2602.18178/) (2026)
- [Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization](https://mlsanity.com/paper/2602.18731/) (2026)
- [Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data](https://mlsanity.com/paper/2608.13575/) (2026)
- [A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization](https://mlsanity.com/paper/2308.05640/) (2023)
- [VortexViz: Finding Vortex Boundaries by Learning from Particle Trajectories](https://mlsanity.com/paper/2404.01352/) (2024)
- [Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities](https://mlsanity.com/paper/2603.05542/) (2026)

## Abstract (arXiv)

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io