Reproducibility and Robustness Questions

Practices and design choices that ensure research results are reproducible and robust. Candidates should discuss experiment logging, seed and randomness control, data and code versioning, deterministic pipelines, checks for statistical validity, sensitivity and stress tests, cross dataset or cross platform replication, and ways to diagnose brittle behavior. Expect discussion of how to design experiments and reporting that enable others to reproduce results and evaluate robustness.