# Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning

arXiv: https://arxiv.org/abs/2402.15997 · HTML: https://mlsanity.com/paper/2402.15997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 77 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Asking Easy Questions: A User-Friendly Approach to Active Reward Learning](https://mlsanity.com/paper/1910.04365/) (2019)
- [VizLinter: A Linter and Fixer Framework for Data Visualization](https://mlsanity.com/paper/2108.10299/) (2021)
- [InfoColorizer: Interactive Recommendation of Color Palettes for Infographics](https://mlsanity.com/paper/2102.02041/) (2021)
- [APReL: A Library for Active Preference-based Reward Learning Algorithms](https://mlsanity.com/paper/2108.07259/) (2021)
- [Learning an Urban Air Mobility Encounter Model from Expert Preferences](https://mlsanity.com/paper/1907.05575/) (2019)
- [Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology](https://mlsanity.com/paper/2007.13872/) (2020)
- [Color Crafting: Automating the Construction of Designer Quality Color Ramps](https://mlsanity.com/paper/1908.00629/) (2019)
- [Semantic Discriminability for Visual Communication](https://mlsanity.com/paper/2009.03171/) (2020)
- [Automatic Scatterplot Design Optimization for Clustering Identification](https://mlsanity.com/paper/2207.03355/) (2022)
- [BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations](https://mlsanity.com/paper/2207.14147/) (2022)
- [Effects of data distribution and granularity on color semantics for colormap data visualizations](https://mlsanity.com/paper/2309.00131/) (2023)

## Abstract (arXiv)

Quality colormaps can help communicate important data patterns. However, finding an aesthetically pleasing colormap that looks "just right" for a given scenario requires significant design and technical expertise. We introduce Cieran, a tool that allows any data analyst to rapidly find quality colormaps while designing charts within Jupyter Notebooks. Our system employs an active preference learning paradigm to rank expert-designed colormaps and create new ones from pairwise comparisons, allowing analysts who are novices in color design to tailor colormaps to their data context. We accomplish this by treating colormap design as a path planning problem through the CIELAB colorspace with a context-specific reward model. In an evaluation with twelve scientists, we found that Cieran effectively modeled user preferences to rank colormaps and leveraged this model to create new quality designs. Our work shows the potential of active preference learning for supporting efficient visualization design optimization.