ICLR Reproducibility Interview #2: Arnout Devos, Sylvain Chatel, Matthias Grossglauser

Arnout Devos and his team, comprising Sylvain Chatel and Matthias Grossglauser from the Swiss Federal Institute of Technology in Lausanne (EPFL), participated in a Reproducibility Challenge driven by an interest in the importance of reproducibility and meta-learning. Motivated by a presentation from Joelle Pineau on reproducibility, they selected a paper on meta-learning, specifically focusing on the R2D2 algorithm, and attempted to reproduce its results despite challenges such as missing parameters and time constraints. They based their approach on the Model-Agnostic Meta-Learning (MAML) framework, though they encountered initial difficulties in aligning with previous results due to variability in seeding and dataset acquisition. In their efforts, they communicated with the original authors, who subsequently clarified and updated their paper, exemplifying the iterative nature of scientific research. The experience underscored the importance of clear documentation and reproducibility in research, influencing Arnout to prioritize these aspects in his future work.

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