ICLR Reproducibility Interview #1: Francesco, Samuel, Emiljano

In a detailed interview, masters students at École polytechnique fédérale de Lausanne (EPFL) shared their experiences participating in the Reproducibility Challenge, where they attempted to reproduce the findings of the paper "Learning Neural PDE Solvers with Convergence Guarantees." Despite successfully replicating the first set of experiments, they faced challenges with the more complex second approach due to missing details about the data, training processes, and model specifications. The students emphasized the importance of clear documentation, accessible code, and detailed experiment setups to ensure reproducibility in research. They discussed the significance of reproducibility in scientific work, noting that it enhances the reliability of research findings and can be particularly challenging when data and code are not openly shared. The students also reflected on how the challenge influenced their approach to research, underscoring the necessity of transparency and the potential benefits of reproducibility initiatives like those seen in recent conferences requiring reproducibility checklists.

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