Machine Learning (Theory)


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 Or, could it be withdrawn later?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


ICML is changing its constitution

Andrew McCallum has been leading an initiative to update the bylaws of IMLS, the organization which runs ICML. I expect most people aren’t interested in such details. However, the bylaws change rarely and can have an impact over a long period of time so they do have some real importance. I’d like to hear comment from anyone with a particular interest before this year’s ICML.

In my opinion, the most important aspect of the bylaws is the at-large election of members of the board which is preserved. Most of the changes between the old and new versions are aimed at better defining roles, committees, etc… to leave IMLS/ICML better organized.

Anyways, please comment if you have a concern or thoughts.


An ICML proposal: yearly surveys

I’d like to propose that ICML conducts a yearly survey similar to the one from 2010 or 2012 which is reported to all.

The key reason for this is information: I expect everyone participating in ICML has some baseline interest in how ICML is doing. Everyone involved has personal anecdotal information, but we all understand that a few examples can be highly misleading.

Aside from satisfying everyone’s joint curiousity, I believe this could improve ICML itself. Consider for example reviewing. Every program chair comes in with ideas for how to make reviewing better. Some succeed, but nearly all are forgotten by the next round of program chairs. Making survey information available will help quantify success and correlate it with design decisions.

The key question to ask for this is “who?” The reason why surveys don’t happen more often is that it has been the responsibility of program chairs who are typically badly overloaded. I believe we should address this by shifting the responsibility to a multiyear position, similar to or the same as a webmaster. This may imply a small cost to the community (<$1/participant) for someone’s time to do and record the survey, but I believe it’s a worthwhile cost. I plan to bring this up with IMLS board in Beijing, but would like to invite any comments or thoughts.


2010 ICML discussion site

A substantial difficulty with the 2009 and 2008 ICML discussion system was a communication vacuum, where authors were not informed of comments, and commenters were not informed of responses to their comments without explicit monitoring. Mark Reid has setup a new discussion system for 2010 with the goal of addressing this.

Mark didn’t want to make it to intrusive, so you must opt-in. As an author, find your paper and “Subscribe by email” to the comments. As a commenter, you have the option of providing an email for follow-up notification.


Languages of Learning

Tags: Organization jl@ 1:08 pm

A language is a set of primitives which can be combined to succesfully create complex objects. Languages arise in all sorts of situations: mechanical construction, martial arts, communication, etc… Languages appear to be the key to succesfully creating complex objects—it is difficult to come up with any convincing example of a complex object which is not built using some language. Since languages are so crucial to success, it is interesting to organize various machine learning research programs by language.

The most common language in machine learning are languages for representing the solution to machine learning. This includes:

  1. Bayes Nets and Graphical Models A language for representing probability distributions. The key concept supporting modularity is conditional independence. Michael Kearns has been working on extending this to game theory.
  2. Kernelized Linear Classifiers A language for representing linear separators, possibly in a large space. The key form of modularity here is kernelization.
  3. Neural Networks A language for representing and learning functions. The key concept supporting modularity is backpropagation. (Yann LeCun gave some very impressive demos at the Chicago MLSS.)
  4. Decision Trees Another language for representing and learning functions. The key concept supporting modularity is partitioning the input space.

Many other learning algorithms can be seen as falling into one of the above families.

In addition there are languages related to various aspects of learning.

  1. Reductions A language for translating between varying real-world losses and core learning algorithm optimizations.
  2. Feature Languages Exactly how features are specified varies from on learning algorithm to another. Several people have been working on languages for features that cope with sparsity or the cross-product nature of databases.
  3. Data interaction languages The statistical query model of learning algorithms provides a standardized interface between data and learning algorithm.

These lists surely miss some languages—feel free to point them out below.

With respect to research “interesting” language-related questions include:

  1. For what aspects of learning is a language missing? Anytime adhocery is encountered, this suggests that there is room for a language. Finding what is not there is both hard and valuable.
  2. Are any of these languages fundamentally flawed or fundamentally advantageous with respect to another language?
  3. What are the most easy to use and effective primitives for these languages?
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