Coronavirus and Machine Learning Conferences

I’ve been following the renamed COVID-19 epidemic closely since potential exponentials deserve that kind of attention.

The last few days have convinced me it’s a good idea to start making contingency plans for machine learning conferences like ICML. The plausible options happen to be structurally aligned with calls to enable reduced travel to machine learning conferences, but of course the need is much more immediate.

I’ll discuss relevant observations about COVID-19 and then the impact on machine learning conferences.

COVID-19 observations

  1. COVID-19 is capable of exponentiating with a base estimated at 2.13-3.11 and a doubling time around a week when unchecked.
  2. COVID-19 is far more deadly than the seasonal flu with estimates of a 2-3% fatality rate but also much milder than SARS or MERS. Indeed, part of what makes COVID-19 so significant is the fact that it is mild for many people leading to a lack of diagnosis, more spread, and ultimately more illness and death.
  3. COVID-19 can be controlled at a large scale via draconian travel restrictions. The number of new observed cases per day peaked about 2 weeks after China’s lockdown and has been declining for the last week.
  4. COVID-19 can be controlled at a small scale by careful contact tracing and isolation. There have been hundreds of cases spread across the world over the last month which have not created new uncontrolled outbreaks.
  5. New significant uncontrolled outbreaks in Italy, Iran, and South Korea have been revealed over the last few days. Some details:
    1. The 8 COVID-19 deaths in Iran suggests that the few reported cases (as of 2/23) are only the tip of the iceberg.
    2. The fact that South Korea and Italy can suddenly discover a large outbreak despite heavy news coverage suggests that it can really happen anywhere.
    3. These new outbreaks suggest that in a few days COVID-19 is likely to become a world-problem with a declining China aspect rather than a China-problem with ramifications for the rest of the world.

There remains quite a bit of uncertainty about COVID-19, of course. The plausible bet is that the known control measures remain effective when and where they can be exercised with new ones (like a vaccine) eventually reducing it to a non-problem.

Conferences
The plausible scenario leaves conferences still in a delicate position because they require many things go right to function. We can easily envision 3 quite different futures here consistent with the plausible case.

  1. Good case New COVID-19 outbreaks are systematically controlled via proven measures with the overall number of daily cases declining steadily as they are right now. The impact on conferences is marginal with lingering travel restrictions affecting some (<10%) potential attendees.
  2. Poor case Multiple COVID-19 outbreaks turn into a pandemic (=multi-continent epidemic) in regions unable to effectively exercise either control measure. Outbreaks in other regions occur, but they are effectively controlled. The impact on conferences is significant with many (50%?) avoiding travel due to either restrictions or uncertainty about restrictions.
  3. Bad case The same as (2), except that an outbreak occurs in the area of the conference. This makes the conference nonviable due to travel restrictions alone. It’s notable here that Italy’s new outbreak involves travel lockdowns a few hundred miles/kilometers from Vienna where ICML 2020 is planned.

Even the first outcome could benefit from some planning while gracefully handling the last outcome requires it.

The obvious response to these plausible scenarios is to reduce the dependence of a successful conference on travel. To do this we need to think about what a conference is in terms of the roles that it fulfills. The quick breakdown I see is:

  1. Distilling knowledge. Luckily, our review process is already distributed.
  2. Passing on knowledge.
  3. Meeting people, both old friends and discovering new ones.
  4. Finding a job / employee.

How (and which) of these can be effectively supported remotely?

I’m planning to have discussions over the next few weeks about this to distill out some plans. If you have good ideas, let’s discuss. Unlike most contingency planning, it seems likely that efforts are not wasted no matter what the outcome 🙂

Updates for the new decade

This blog has been quiet for the last year. I have quite a bit to write about but found myself often out of time between work at Microsoft, ICML duties, and family life. Nevertheless, I expect to get back to more substantive discussions as I adjust to the new load.

In the meantime, I’ve updated the site in various ways: SSL now works, and mail for people registering new accounts should work again.

I also setup a twitter account as I’ve often had things left unsaid. I’m not a fan of blog-by-twitter (which seems artificially disjointed), so I expect to use twitter for shorter things and hunch.net for longer things.

ICML has 3(!) Real World Reinforcement Learning Workshops

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.

Code submission should be encouraged but not compulsory

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.

FAQ on ICML 2019 Code Submission Policy

ICML 2019 has an option for supplementary code submission that the authors can use to provide additional evidence to bolster their experimental results. Since we have been getting a lot of questions about it, here is a Frequently Asked Questions for authors.

1. Is code submission mandatory?

No. Code submission is completely optional, and we anticipate that high quality papers whose results are judged by our reviewers to be credible will be accepted to ICML, even if code is not submitted.

2. Does submitted code need to be anonymized?

ICML is a double blind conference, and we expect authors to put in reasonable effort to anonymize the submitted code and institution. This means that author names and licenses that reveal the organization of the authors should be removed.

Please note that submitted code will not be made public — eg, only the reviewers, Area Chair and Senior Area Chair in charge will have access to it during the review period. If the paper gets accepted, we expect the authors to replace the submitted code by a non-anonymized version or link to a public github repository.

3. Are anonymous github links allowed?

Yes. However, they have to be on a branch that will not be modified after the submission deadline. Please enter the github link in a standalone text file in a submitted zip file.

4. How will the submitted code be used for decision-making?

The submitted code will be used as additional evidence provided by the authors to add more credibility to their results. We anticipate that high quality papers whose results are judged by our reviewers to be credible will be accepted to ICML, even if code is not submitted. However, if something is unclear in the paper, then code, if submitted, will provide an extra chance to the authors to clarify the details. To encourage code submission, we will also provide increased visibility to papers that submit code.

5. If code is submitted, do you expect it to be published with the rest of the supplementary? Or, could it be withdrawn later?

We expect submitted code to be published with the rest of the supplementary. However, if the paper gets accepted, then the authors will get a chance to update the code before it is published by adding author names, licenses, etc.

6. Do you expect the code to be standalone? For example, what if it is part of a much bigger codebase?

We expect your code to be readable and helpful to reviewers in verifying the credibility of your results. It is possible to do this through code that is not standalone — for example, with proper documentation.

7. What about pseudocode instead of code? Does that count as code submission?

Yes, we will count detailed pseudocode as code submission as it is helpful to reviewers in validating your results.

8. Do you expect authors to submit data?

We understand that many of our authors work with highly sensitive datasets, and are not asking for private data submission. If the dataset used is publicly available, there is no need to provide it. If the dataset is private, then the authors can submit a toy or simulated dataset to illustrate how the code works.

9. Who has access to my code?

Only the reviewers, Area Chair and Senior Area Chair assigned to your paper will have access to your code. We will instruct reviewers, Area Chair and Senior Area Chair to keep the code submissions confidential (just like the paper submissions), and delete all code submissions from their machine at the end of the review cycle. Please note that code submission is also completely optional.

10. I would like to revise my code/add code during author feedback. Is this permitted?

Unfortunately, no. But please remember that code submission is entirely optional.

The detailed FAQ as well other Author and Style instructions are available here.

Kamalika Chaudhuri and Ruslan Salakhutdinov
ICML 2019 Program Chairs