Lars Doorenbos
University of Bern
Bern, Switzerland
I am a Ph.D student currently based in Bern, Switzerland, where I work on creating safe and reliable deep learning models through out-of-distribution detection. Before, I completed my Bachelor’s and Master’s degree in Computing Science at the University of Groningen.
news
Nov 15, 2024 | I completed my PhD! |
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Oct 28, 2024 | Our work Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models was accepted at The Astrophysical Journal. |
Jul 10, 2024 | Our work on predicting galaxy spectra from photometry was accepted as an oral at the International Conference on Machine Learning for Astrophysics. I will also give a flash talk on ULISSE. |
Jul 1, 2024 | Our paper “Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection” has been accepted at the European Conference on Computer Vision. |
Jun 30, 2024 | Our paper “SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants” has been accepted at the International Conference on Intelligent Robots and Systems. It was also featured in a New Scientist article and the C4SR+ workshop at ICRA 2024. |
May 21, 2024 | Our paper “Hyperbolic Random Forests” has been published in Transactions on Machine Learning Research. |
Apr 2, 2024 | I have been selected as a DAAD AInet fellow for 2024. |
Nov 10, 2023 | I finished my internship at Nvidia, where I worked on controlling robots with vision-language models. A preprint of my work is currently under review. |
Jul 18, 2023 | Our paper “Stochastic Segmentation with Conditional Categorical Diffusion Models” has been accepted at ICCV 2023. |
Apr 20, 2023 | “Generating astronomical spectra from photometry with conditional diffusion models” has been selected as an oral presentation at the Bern Data Science Day 2023. |
selected publications
2024
- Learning Non-Linear Invariants for Unsupervised Out-of-Distribution DetectionEuropean Conference on Computer Vision, 2024
- Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion modelsarXiv preprint arXiv:2406.18175, 2024*equal contribution
- Hyperbolic Random ForestsTransactions on Machine Learning Research, 2024
2023
- Stochastic Segmentation with Conditional Categorical Diffusion ModelsInternational Conference on Computer Vision, 2023*equal contribution
2022
- Generating astronomical spectra from photometry with conditional diffusion modelsNeurIPS: Machine Learning for the Physical Sciences Workshop, 2022
- Data invariants to understand unsupervised out-of-distribution detectionIn European Conference on Computer Vision, 2022