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Semantic Software Lab
Concordia University
Montréal, Canada

Machine Learning Blog

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Machine learning and learning theory research
Updated: 23 hours 26 min ago

ICML has 3(!) Real World Reinforcement Learning Workshops

Fri, 2019-06-07 11:28

The first is Sunday afternon during the Industry Expo day. This one is meant to be quite practical, starting with an overview of Contextual Bandits and leading into how to apply the new Personalizer service, the first service in the world functionally supporting general contextual bandit learning.

The second is Friday morning. This one is more academic with many topics. I’ll personally be discussing research questions for real world RL.

The third one is Friday afternoon with more emphasis on sequences of decisions. I expect to here “imitation learning” multiple times

I’m planning to attend all 3. It’s great to see interest building in this direction, because Real World RL seems like the most promising direction for fruitfully expanding the scope of solvable machine learning problems.

Categories: Blogroll

Code submission should be encouraged but not compulsory

Tue, 2019-02-26 12:27

ICML, ICLR, and NeurIPS are all considering or experimenting with code and data submission as a part of the reviewer or publication process with the hypothesis that it aids reproducibility of results. Reproducibility has been a rising concern with discussions in paper, workshop, and invited talk.

The fundamental driver is of course lack of reproducibility. Lack of reproducibility is an inherently serious and valid concern for any kind of publishing process where people rely on prior work to compare with and do new things. Lack of reproducibility (due to random initialization for example) was one of the things leading to a period of unpopularity for neural networks when I was a graduate student. That has proved nonviable (Surprise! Learning circuits is important!), but the reproducibility issue remains. Furthermore, there is always an opportunity and latent suspicion that authors ‘cheat’ in reporting results which could be allayed using a reproducible approach.

With the above said, I think the reproducibility proponents should understand that reproducibility is a value but not an absolute value. As an example here, I believe it’s quite worthwhile for the community to see AlphaGoZero published even if the results are not necessarily easily reproduced. There is real value for the community in showing what is possible irrespective of whether or not another game with same master of Go is possible, and there is real value in having an algorithm like this be public even if the code is not. Treating reproducibility as an absolute value could exclude results like this.

An essential understanding here is that machine learning is (at least) 3 different kinds of research.

  • Algorithms: The goal is coming up with a better algorithm for solving some category of learning problems. This is the most typical viewpoint at these conferences.
  • Theory: The goal is generally understanding what is possible or not possible for learning algorithms. Although these papers may have algorithms, they are often not the point and demanding an implementation of them is a waste of time for author, reviewer, and reader.
  • Applications: The goal is solving some particular task. AlphaGoZero is a reasonable example of this—it was about beating the world champion in Go with algorithmic development in service of that. For this kind of research perfect programmatic reproducibility may be infeasible because the computation is to extreme, the data is proprietary, etc…

Using a one-size-fits-all approach where you demand that every paper “is” a programmatically reproducible implementation is a mistake that would create a division that reduces our community. Keeping this three-fold focus fundamentally enriches the community both literally and ontologically.

Another view here is provided by considering the argument at a wider scope. Would you prefer that health regulations/treatments be based on all scientific studies including those where data is not fully released to the public (i.e almost all of them for privacy reasons)? Or would you prefer that health regulations/treatments be based only on data fully released to the public? Preferring the latter is equivalent to ignoring most scientific studies in making decisions.

The alternative to a compulsory approach is to take an additive view. The additive approach has a good track record amongst reviewing process changes.

  • When I was a graduate student, papers were not double blind. The community switched to double blind because it adds an opportunity for reviewers to review fairly and it gives authors a chance to have their work reviewed fairly whether they are junior or senior. As a community we also do not restrict posting on arxiv or talks about a paper before publication, because that would subtract from what authors can do. Double blind reviewing could be divisive, but it is not when used in this fashion.
  • When I was a graduate student, there was also a hard limit on the number of pages in submissions. For theory papers this meant that proofs were not included. We changed the review process to allow (but not require) submission of an appendix which could optionally be used by reviewers. This again adds to the options available to authors/reviewers and is generally viewed as positive by everyone involved.

What can we add to the community in terms reproducibility?

  1. Can reviewers do a better job of reviewing if they have access to the underlying code or data?
  2. Can authors benefit from releasing code?
  3. Can readers of a paper benefit from an accompanying code release?

The answer to each of these question is a clear ‘yes’ if done right.

For reviewers, it’s important to not overburden them. They may lack the computational resources, platform, or personal time to do a full reproduction of results even if that is possible. Hence, we should view code (and data) submission in the same way as an appendix which reviewers may delve into and use if they so desire.

For authors, code release has two benefits—it provides an additional avenue for convincing reviewers who default to skeptical and it makes followup work significantly more likely. My most cited paper was Isomap which did indeed come with a code release. Of course, this is not possible or beneficial for authors in many cases. Maybe it’s a theory paper where the algorithm isn’t the point? Maybe either data or code can’t be fully released since it’s proprietary? There are a variety of reasons. From this viewpoint we see that releasing code should be supported and encouraged but optional.

For readers, having code (and data) available obviously adds to the depth of value that a paper has. Not every reader will take advantage of that but some will and it enormously reduces the barrier to using a paper in many cases.

Let’s assume we do all of these additive and enabling things, which is about where Kamalika and Russ aimed the ICML policy this year.

Is there a need for go further towards compulsory code submission? I don’t yet see evidence that default skeptical reviewers aren’t capable of weighing the value of reproducibility against other values in considering whether a paper should be published.

Should we do less than the additive and enabling things? I don’t see why—the additive approach provides pure improvements to the author/review/publish process. Not everyone is able to take advantage of this, but that seems like a poor reason to restrict others from taking advantage when they can.

One last thing to note is that this year’s code submission process is an experiment. We should all want program chairs to be able to experiment, because that is how improvements happen. We should do our best to work with such experiments, try to make a real assessment of success/failure, and expect adjustments for next year.

Categories: Blogroll