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Future Publication Models @ NIPS

Yesterday, there was a discussion about future publication models at NIPS. Yann and Zoubin have specific detailed proposals which I’ll add links to when I get them (Yann’s proposal and Zoubin’s proposal).

What struck me about the discussion is that there are many simultaneous concerns as well as many simultaneous proposals, which makes it difficult to keep all the distinctions straight in a verbal conversation. It also seemed like people were serious enough about this that we may see some real movement. Certainly, my personal experience motivates that as I’ve posted many times about the substantial flaws in our review process, including some very poor personal experiences.

Concerns include the following:

  1. (Several) Reviewers are overloaded, boosting the noise in decision making.
  2. (Yann) A new system should run with as little built-in delay and friction to the process of research as possible.
  3. (Hanna Wallach(updated)) Double-blind review is particularly important for people who are unknown or from an unknown institution.
  4. (Several) But, it’s bad to take double blind so seriously as to disallow publishing on arxiv or personal webpages.
  5. (Yann) And double-blind is bad when it prevents publishing for substantial periods of time. Apparently, this comes up in CVPR.
  6. (Zoubin) Any new system should appear to outsiders as if it’s the old system, or a journal, because it’s already hard enough to justify CS tenure cases to other disciplines.
  7. (Fernando) There shouldn’t be a big change with a complex bureaucracy, but rather a smaller changes which are obviously useful or at least worth experimenting with.

There were other concerns as well, but these are the ones that I remember.

Elements of proposals include:

  1. (Yann) Everything should go to Arxiv or an arxiv-like system first, as per physics or mathematics. This addresses (1), because it delinks dissemination from review, relieving some of the burden of reviewing. It also addresses (2) since with good authors they can immediately begin building on each other’s work. It conflicts with (3), because Arxiv does not support double-blind submission. It does not conflict if we build our own system.
  2. (Fernando) Create a conference coincident journal in which people can publish at any time. VLDB has apparently done this. It can be done smoothly by allowing submission in either conference deadline mode or journal mode. This proposal addresses (1) by reducing peak demand on reviewing. It also addresses (6) above.
  3. (Daphne) Perhaps we should have a system which only reviews papers for correctness, which is not nearly as subjective as for novelty or interestingness. This addresses (1), by eliminating some concerns for the reviewer. It is orthogonal to the double blind debate. In biology, such a journal exists (pointer updated), because delays were becoming absurd and intolerable.
  4. (Yann) There should be multiple publishing entities (people or groups of people) that can bless a paper as interesting. This addresses (1).

There are many other proposal elements (too many for my memory), which hopefully we’ll see in particular proposals. If other people have concrete proposals, now is probably the right time to formalize them.

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NIPS workshops

Many of the NIPS workshops have a deadline about now, and the NIPS early registration deadline is Nov. 6. Several interest me:

  1. Adaptive Sensing, Active Learning, and Experimental Design due 10/27.
  2. Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra, due Nov. 6.
  3. Large-Scale Machine Learning: Parallelism and Massive Datasets, due 10/23 (i.e. past)
  4. Analysis and Design of Algorithms for Interactive Machine Learning, due 10/30.

And I’m sure many of the others interest others. Workshops are great as a mechanism for research, so take a look if there is any chance you might be interested.

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ALT 2009

I attended ALT (“Algorithmic Learning Theory”) for the first time this year. My impression is ALT = 0.5 COLT, by attendance and also by some more intangible “what do I get from it?” measure. There are many differences which can’t quite be described this way though. The program for ALT seems to be substantially more diverse than COLT, which is both a weakness and a strength.

One paper that might interest people generally is:

Alexey Chernov and Vladimir Vovk, Prediction with Expert Evaluators’ Advice. The basic observation here is that in the online learning with experts setting you can simultaneously compete with several compatible loss functions simultaneously. Restated, debating between competing with log loss and squared loss is a waste of breath, because it’s almost free to compete with them both simultaneously. This might interest anyone who has run into “which loss function?” debates that come up periodically.

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Interesting papers at UAICMOLT 2009

Here’s a list of papers that I found interesting at ICML/COLT/UAI in 2009.

  1. Elad Hazan and Comandur Seshadhri Efficient learning algorithms for changing environments at ICML. This paper shows how to adapt learning algorithms that compete with fixed predictors to compete with changing policies. The definition of regret they deal with seems particularly useful in many situation.
  2. Hal Daume, Unsupervised Search-based Structured Prediction at ICML. This paper shows a technique for reducing unsupervised learning to supervised learning which (a) make a fast unsupervised learning algorithm and (b) makes semisupervised learning both easy and highly effective.
  3. There were two papers with similar results on active learning in the KWIK framework for linear regression, both reducing the sample complexity to . One was Nicolo Cesa-Bianchi, Claudio Gentile, and Francesco Orabona Robust Bounds for Classification via Selective Sampling at ICML and the other was Thomas Walsh, Istvan Szita, Carlos Diuk, Michael Littman Exploring compact reinforcement-learning representations with linear regression at UAI. The UAI paper covers application to RL as well.
  4. Ping Li, Improving Compressed Counting at UAI. This paper talks about how to keep track of the moments in a datastream with very little space and computation. I’m not sure I have a use for it yet, but it seems like a cool piece of basic technology.
  5. Mark Reid and Bob Williamson Surrogate Regret Bounds for Proper Losses at ICML. This paper points out that via the integral characterization of proper losses, proper scoring rules can be reduced to binary classification. The results unify and generalize the Probing and Quanting reductions we worked on previously. This paper is also related to Nicolas Lambert‘s work, which is quite thought provoking in terms of specifying what is learnable.
  6. Daniel Hsu, Sham M. Kakade and Tong Zhang. A Spectral Algorithm for Learning Hidden Markov Models COLT. This paper shows that a subset of HMMs can be learned using an SVD-based algorithm.
  7. Samory Kpotufe, Escaping the curse of dimensionality with a tree-based regressor at COLT. This paper shows how to directly applying regression in high dimensional vector spaces and have it succeed anyways because the data is naturally low-dimensional.
  8. Shai Ben-David, David Pal and Shai Shalev-Shwartz. Agnostic Online Learning at COLT. This paper characterizes the ability to learn when an adversary is choosing features in the online setting as the “Littlestone dimension”.
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2009 ICML discussion site

Mark Reid has setup a discussion site for ICML papers again this year and Monica Dinculescu has linked it in from the ICML site. Last year’s attempt appears to have been an acceptable but not wild success as a little bit of fruitful discussion occurred. I’m hoping this year will be a bit more of a success—please don’t be shy 🙂

I’d like to also point out that ICML‘s early registration deadline has a few hours left, while UAI‘s and COLT‘s are in a week.