Comment by XuMiao | Hacker News Reader

Comment by XuMiao | Hacker News Reader

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I used to believe that generalization is about some nice concentration bound that one can design carefully in terms of the optimization algorithm. After many years practice, I began to firmly question about the basic assumption about data distributions. For many real applications, assuming the data are from the same unknown true distribution is fundamentally incomplete. In the real world, information is always partial and adversarials always exist. It never is a closed world where adding samples eventually has diminished value. Instead, it's an open world where long tail phenomenons are often observed. It never is about a single objective that the optimization algorithm cares about. Instead, it's entangled with many coupled but conflicting interests.

The existing statistical learning theory is having a hard time on addressing questions like combinatorial generalizability. Hopefully, the new stability analysis and adversarial analysis can lead us somewhere theoretically.

I’ve had the opposite experience. After finishing grad school and working in industry for a long time now, applying ML to computer vision, NLP, causal inference and quant finance for production systems, the vast, vast majority of problems are such that the training and test set are defined by very precise business operating constraints and it’s known with a high degree of certainty that future examples to which the model is required to generalize will also be from the same distribution reflected in model training, and any differences are so minor as to be practically ignorable all the time, and requiring really special evidence to show the super rare cases when it cannot be ignored.

(Those cases also are most often just solved by collecting new/ more training & testing data and re-training / fine-tuning / calibrating the model to some slight shift in the distribution, and require no formal modeling techniques related to tricky generalization distribution differences).

This has been true across commercial ML products or services at large tech companies, small startups, quant finance firms, defense research labs, and academic labs. Across projects ranging from face recognition, high-traffic search engine and related services like machine translation, trending models, search ranking, dimensionality reduction models in time series forecasting, reverse image search, audio similarity models, advertising models, and many more.

It just turns out that in industry, the problems that have meaningful utility to the business are almost always defined by intrinsic constraints that nearly guarantee there won’t be a meaningful difference in the distribution of future prediction-time examples compared with training & testing distribution.

I find all this very hard to square with my understanding. I mean, what kind of "business operating constraints" stay the same over months or years? Or do you mean that, when constraints change, you just train a new model?

I think basically what you're saying is "we choose the data we want to reprsent, so we know when we're representing it well". But that's just what I say above, about measuring progress by hitting the targets you choose, completely arbitrarily.

It's as if I shot a bunch of arrows and then painted targets around them, then claimed I'm a great archer because I always hit bullseye.

I don't think that's the result of some fundamental difference in understanding between academia and industry, either. Perhaps, in the industry, assuming that reality will comply with your assumptions helps achieve business aims, but that's not because the systems work. It's becaus they can be made to look like they do and there's no good way to show they don't, as there's no good way to show they do.

It sounds like you haven’t worked on many real world applied problems.

I would add that I have had my fair share of industry scale problems where labeled data is not drawn from the same distribution as where the system will be deployed. But as you pointed out earlier, there are theoretical and practical methods to deal with this. For someone who wants to get familiar with this territory, domain adaptation, transfer learning, transductive learning, semi-supervised learning, etc would be some of the keywords to seed an initial literature search with.

If you mean that measuring generalisation is a poorly defined problem and we need to develop new theoretical tools- I'm totally with you. Unfortunately, in machine learning, the ratio of theory to practice is 1:100, or so.

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