Machine Learning (Theory)


Both new: STOC workshops and NEML

May 16 in Cambridge, is the New England Machine Learning Day, a first regional workshop/symposium on machine learning. To present a poster, submit an abstract by May 5.

May 19 in New York, STOC is coming to town and rather surprisingly having workshops which should be quite a bit of fun. I’ll be speaking at Algorithms for Distributed and Streaming Data.


ICML author feedback is open

Tags: Conferences,Machine Learning jl@ 8:24 pm

as of last night, late.

When the reviewing deadline passed Wednesday night 15% of reviews were still missing, much higher than I expected. Between late reviews coming in, ACs working overtime through the weekend, and people willing to help in the pinch another ~390 reviews came in, reducing the missing mass to 0.2%. Nailing that last bit and a similar quantity of papers with uniformly low confidence reviews is what remains to be done in terms of basic reviews. We are trying to make all of those happen this week so authors have some chance to respond.

I was surprised by the quantity of late reviews, and I think that’s an area where ICML needs to improve in future years. Good reviews are not done in a rush—they are done by setting aside time (like an afternoon), and carefully reading the paper while thinking about implications. Many reviewers do this well but a significant minority aren’t good at scheduling their personal time. In this situation there are several ways to fail:

  1. Give early warning and bail.
  2. Give no warning and finish not-too-late.
  3. Give no warning and don’t finish.

The worst failure mode by far is the last one for Program Chairs and Area Chairs, because they must catch and fix all the failures at the last minute. I expect the second failure mode also impacts the quality of reviews because high speed reviewing of a deep paper often doesn’t work. This issue is one of community norms which can only be adjusted slowly. To do this, we’re going to pass a flake list for failure mode 3 to future program chairs who will hopefully further encourage people to schedule time well and review carefully.

If my experience is any guide, plenty of authors will feel disappointed by the reviews. Part of this is simply because it’s the first time the authors have had contact with people not biased towards agreeing with them, as almost all friends are. Part of this is the significant hurdle of communicating technical new things well. Part may be too-hasty reviews, as discussed above. And part of it may be that the authors simply are far more expert in their subject than reviewers.

In author responses, my personal tendency is to be blunter than most people when reviewers make errors. Perhaps “kind but clear” is a good viewpoint. You should be sympathetic to reviewers who have voluntarily put significant time into reviewing your paper, but you should also use the channel to communicate real information. Remotivating your paper almost never works, so concentrate on getting across errors in understanding by reviewers or answer their direct questions.

We did not include reviewer scores in author feedback, although we do plan to include them when the decision is made. Scores should not be regarded as final by any party, since author feedback and discussion can significantly alter a reviewer’s understanding of the paper. Encouraging reviewers to incorporate this additional information well before settling on a final score is one of my goals.

We did allow resubmission of the paper with the author response, similar to what Geoff Gordon did as program chair for AIStat. This solves two problems: It helps authors create a more polished draft, and it avoids forcing an overly constrained channel in the communication. If an equation has a bug, you can write it out bug free in mathematical notation rather than trying to describe by reference how to alter the equation in author response.

Please comment if you have further thoughts.

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