My research interests include few-shot learning, probabilistic modeling, causal inference, and automating machine learning.
Previously, I obtained my MS in CS at USC 🇺🇸, and MS/BS in EECS at KU Leuven 🇧🇪. I have been a (research) intern at Amazon, Stanford, EPFL, and Siemens.
🎓 Google Scholar · 🧑💻 GitHub · 🦜 Twitter · 🔗 LinkedIn · 📍 EPFL BC264
Don’t hesitate to drop me an email at ✉️ [first].[last]@epfl.ch
** Denotes equal contribution*
**Model-Agnostic Learning to Meta-Learn Arnout Devos***, Yatin Dandi* **Proceedings of Machine Learning Research (PMLR), Volume 148, 2021
A Meta-Learning Approach for Genomic Survival Analysis Yeping Lina Qiu, Hong Zheng, Arnout Devos, Heather Selby, Olivier Gevaert **Nature Communications, Volume 11, 2020
Regression Networks for Meta-Learning Few-Shot Classification Arnout Devos, Matthias Grossglauser **ICML Workshop on Automated Machine Learning (AutoML), 2020
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification Carlos Medina*, Arnout Devos*, Matthias Grossglauser **ICML Workshop on Automated Machine Learning (AutoML), 2020
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