{"id":340,"date":"2008-07-10T02:10:45","date_gmt":"2008-07-10T08:10:45","guid":{"rendered":"http:\/\/hunch.net\/?p=340"},"modified":"2008-07-11T21:48:17","modified_gmt":"2008-07-12T03:48:17","slug":"interesting-papers-icml-2008","status":"publish","type":"post","link":"https:\/\/hunch.net\/?p=340","title":{"rendered":"Interesting papers, ICML 2008"},"content":{"rendered":"

Here are some papers from ICML 2008<\/a> that I found interesting. <\/p>\n

    \n
  1. Risi Kondor<\/a> and Karsten Borgwardt<\/a>, The Skew Spectrum of Graphs<\/a>. This paper is about a new family of functions on graphs which is invariant under node label permutation. They show that these quantities appear to yield good features for learning.<\/li>\n
  2. Sanjoy Dasgupta<\/a> and Daniel Hsu<\/a>. Hierarchical sampling for active learning.<\/a> This is the first published practical consistent active learning algorithm. The abstract is also pretty impressive.<\/li>\n
  3. Lihong Li<\/a>, Michael Littman<\/a>, and Thomas Walsh<\/a> Knows What It Knows: A Framework For Self-Aware Learning.<\/a> This is an attempt to create learning algorithms that know when they err, (other work includes Vovk<\/a>). It’s not yet clear to me what the right model for feature-dependent confidence intervals<\/a> is.<\/li>\n
  4. Novi Quadrianto<\/a>, Alex Smola<\/a>, TIberio Caetano<\/a>, and Quoc Viet Le<\/a> Estimating Labels from Label Proportions<\/a>. This is an example of learning in a specialization of the offline contextual bandit setting.<\/li>\n
  5. Filip Radlinski<\/a>, Robert Kleinberg<\/a> and Thorsten Joachims<\/a>
    \n    Learning Diverse Rankings with Multi-Armed Bandits.<\/a> Learning should be used to solve the diversity problem, and doing it in an online bandit-like setting is quite natural. I believe the setting can be generalized to a setting with features without too much work.<\/li>\n
  6. Rich Caruana<\/a>, Nikos Karampatziakis<\/a>, Ainur Yessenalina<\/a> An Empirical Evaluation of Supervised Learning in High Dimensions<\/a>. This paper doesn’t need an abstract given the title :). I hadn’t previously appreciated how well a random forest works in high dimensions.<\/li>\n
  7. Sham M. Kakade<\/a>, Shai Shalev-Shwartz<\/a>, and Ambuj Tewari<\/a>. Efficient Bandit Algorithms for Online Multiclass Prediction<\/a>. A paper about an online contextual bandit setting specialized to a multiclass realizable yet otherwise adversarial setting that yields a practical algorithm.<\/li>\n<\/ol>\n

    I’d like to add that I thought the conference organization (and the colocation with COLT<\/a> and UAI<\/a>) are particularly well done, the best I’ve seen. The key seems to be tight integration of the colocating conference programs and hordes of local volunteers making sure everything is working. I was also happy to see a 10 years award<\/a> for best paper 10 years ago.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Here are some papers from ICML 2008 that I found interesting. Risi Kondor and Karsten Borgwardt, The Skew Spectrum of Graphs. This paper is about a new family of functions on graphs which is invariant under node label permutation. They show that these quantities appear to yield good features for learning. Sanjoy Dasgupta and Daniel … <\/p>\n

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