An (incomplete) list of projects that I have (co-)created as part of my research and beyond. Working on a related project and want to collaborate? Reach out!

Python packages
Inverse Constitutional AI (GitHub, paper)
A Python library that implements the Inverse Constitutional AI (ICAI) method, compressing pairwise preference data into a short constitution of principles.
Autoplotlib (GitHub)
A Python library to quickly generate plots in Python by simply describing them through text - e.. use “create a scatter plot with name labels” instead of endlessly searching stackoverflow.
Bauwerk (GitHub, docs)
A meta reinforcement learning (meta RL) benchmark with building control environments. Bauwerk aims to facilitate the development of methods that generalise across buildings to help scale greener building controllers to more buildings.
Beobench (GitHub, docs, paper)
A tool providing easy and unified access to building control environments for reinforcement learning (RL) aiming to enable better comparability and evaluation.
GeoGraph (GitHub, docs)
A tool for analysing habitat fragmentation and related problems in landscape ecology. GeoGraph builds a geospatially referenced graph from land cover or field survey data and enables graph-based landscape ecology analysis as well as interactive visualizations.

Web apps
KraspAI Kompass (website, no longer active)
A tool to make AI capabilities more human interpretable. Understanding new models’ capabilities is hard. Kompass aims to make it easy by providing tiny but highly informative benchmarks.
langlabel (website)
A tool to automatically label language data using the latest language models. No coding required.

Papers

See my Google Scholar profile for the (likely) most up-to-date list.

  1. Findeis, Arduin, Timo Kaufmann, Eyke Hüllermeier, Samuel Albanie, and Robert Mullins. “Inverse Constitutional AI: Compressing Preferences into Principles.” arXiv preprint arXiv:2406.06560. 2024. (pdf)
  2. Ruis, Laura, Arduin Findeis, Herbie Bradley, Hossein A. Rahmani, Kyoung Whan Choe, Edward Grefenstette, and Tim Rocktäschel. “Do LLMs selectively encode the goal of an agent’s reach?.” In First Workshop on Theory of Mind in Communicating Agents at the Fortieth International Conference on Machine Learning (ICML). 2023. (pdf)
  3. Findeis, Arduin, Fiodar Kazhamiaka, Scott Jeen, and Srinivasan Keshav. “Beobench: a toolkit for unified access to building simulations for reinforcement learning.” In Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, pp. 374-382. 2022. (pdf)