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Tom Dietterich's avatar

I don't take my own advice, but my advice is to encapsulate the entire analysis chain in a docker container so that you have the right versions of all libraries, etc. It's obviously not a complete solution, because the container might run differently on different hardware.

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Yaroslav Bulatov's avatar

This issue reminds of the connection between robustness and generalization in ML https://jmlr.csail.mit.edu/papers/v2/bousquet02a.html . If your trained model performs well on train set and you end up with nearly the same model after introducing perturbations to the train set and retraining, then this model will perform well on test set. To stretch the analogy, perhaps the "trained model" can be viewed as the derived theory and different groups replicating the study give perturbations of the training set

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