hunch_net hunch_net-2005 hunch_net-2005-86 knowledge-graph by maker-knowledge-mining
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Introduction: I just presented the cross validation problem at COLT . The problem now has a cash prize (up to $500) associated with it—see the presentation for details. The write-up for colt .
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same-blog 1 1.0 86 hunch net-2005-06-28-The cross validation problem: cash reward
Introduction: I just presented the cross validation problem at COLT . The problem now has a cash prize (up to $500) associated with it—see the presentation for details. The write-up for colt .
2 0.30457109 26 hunch net-2005-02-21-Problem: Cross Validation
Introduction: The essential problem here is the large gap between experimental observation and theoretical understanding. Method K-fold cross validation is a commonly used technique which takes a set of m examples and partitions them into K sets (“folds”) of size m/K . For each fold, a classifier is trained on the other folds and then test on the fold. Problem Assume only independent samples. Derive a classifier from the K classifiers with a small bound on the true error rate. Past Work (I’ll add more as I remember/learn.) Devroye , Rogers, and Wagner analyzed cross validation and found algorithm specific bounds. Not all of this is online, but here is one paper . Michael Kearns and Dana Ron analyzed cross validation and found that under additional stability assumptions the bound for the classifier which learns on all the data is not much worse than for a test set of size m/K . Avrim Blum, Adam Kalai , and myself analyzed cross validation and found tha
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Introduction: By Shie and Nati Following John’s advertisement for submitting to ICML, we thought it appropriate to highlight the advantages of COLT, and the reasons it is often the best place for theory papers. We would like to emphasize that we both respect ICML, and are active in ICML, both as authors and as area chairs, and certainly are not arguing that ICML is a bad place for your papers. For many papers, ICML is the best venue. But for many theory papers, COLT is a better and more appropriate place. Why should you submit to COLT? By-and-large, theory papers go to COLT. This is the tradition of the field and most theory papers are sent to COLT. This is the place to present your ground-breaking theorems and new models that will shape the theory of machine learning. COLT is more focused then ICML with a single track session. Unlike ICML, the norm in COLT is for people to sit through most sessions, and hear most of the talks presented. There is also often a lively discussion followi
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Introduction: The health of COLT (Conference on Learning Theory or Computational Learning Theory depending on who you ask) has been questioned over the last few years. Low points for the conference occurred when EuroCOLT merged with COLT in 2001, and the attendance at the 2002 Sydney COLT fell to a new low. This occurred in the general context of machine learning conferences rising in both number and size over the last decade. Any discussion of why COLT has had difficulties is inherently controversial as is any story about well-intentioned people making the wrong decisions. Nevertheless, this may be worth discussing in the hope of avoiding problems in the future and general understanding. In any such discussion there is a strong tendency to identify with a conference/community in a patriotic manner that is detrimental to thinking. Keep in mind that conferences exist to further research. My understanding (I wasn’t around) is that COLT started as a subcommunity of the computer science
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Introduction: We’ve discussed presentation preparation before , but I have one more thing to add: transitioning . For a research presentation, it is substantially helpful for the audience if transitions are clear. A common outline for a research presentation in machine leanring is: The problem . Presentations which don’t describe the problem almost immediately lose people, because the context is missing to understand the detail. Prior relevant work . In many cases, a paper builds on some previous bit of work which must be understood in order to understand what the paper does. A common failure mode seems to be spending too much time on prior work. Discuss just the relevant aspects of prior work in the language of your work. Sometimes this is missing when unneeded. What we did . For theory papers in particular, it is often not possible to really cover the details. Prioritizing what you present can be very important. How it worked . Many papers in Machine Learning have some sor
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same-blog 1 0.98355228 86 hunch net-2005-06-28-The cross validation problem: cash reward
Introduction: I just presented the cross validation problem at COLT . The problem now has a cash prize (up to $500) associated with it—see the presentation for details. The write-up for colt .
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Introduction: A while ago , we discussed the health of COLT . COLT 2008 substantially addressed my concerns. The papers were diverse and several were interesting. Attendance was up, which is particularly notable in Europe. In my opinion, the colocation with UAI and ICML was the best colocation since 1998. And, perhaps best of all, registration ended up being free for all students due to various grants from the Academy of Finland , Google , IBM , and Yahoo . A basic question is: what went right? There seem to be several answers. Cost-wise, COLT had sufficient grants to alleviate the high cost of the Euro and location at a university substantially reduces the cost compared to a hotel. Organization-wise, the Finns were great with hordes of volunteers helping set everything up. Having too many volunteers is a good failure mode. Organization-wise, it was clear that all 3 program chairs were cooperating in designing the program. Facilities-wise, proximity in time and space made
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Introduction: The essential problem here is the large gap between experimental observation and theoretical understanding. Method K-fold cross validation is a commonly used technique which takes a set of m examples and partitions them into K sets (“folds”) of size m/K . For each fold, a classifier is trained on the other folds and then test on the fold. Problem Assume only independent samples. Derive a classifier from the K classifiers with a small bound on the true error rate. Past Work (I’ll add more as I remember/learn.) Devroye , Rogers, and Wagner analyzed cross validation and found algorithm specific bounds. Not all of this is online, but here is one paper . Michael Kearns and Dana Ron analyzed cross validation and found that under additional stability assumptions the bound for the classifier which learns on all the data is not much worse than for a test set of size m/K . Avrim Blum, Adam Kalai , and myself analyzed cross validation and found tha
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Introduction: The health of COLT (Conference on Learning Theory or Computational Learning Theory depending on who you ask) has been questioned over the last few years. Low points for the conference occurred when EuroCOLT merged with COLT in 2001, and the attendance at the 2002 Sydney COLT fell to a new low. This occurred in the general context of machine learning conferences rising in both number and size over the last decade. Any discussion of why COLT has had difficulties is inherently controversial as is any story about well-intentioned people making the wrong decisions. Nevertheless, this may be worth discussing in the hope of avoiding problems in the future and general understanding. In any such discussion there is a strong tendency to identify with a conference/community in a patriotic manner that is detrimental to thinking. Keep in mind that conferences exist to further research. My understanding (I wasn’t around) is that COLT started as a subcommunity of the computer science
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Introduction: I just presented the cross validation problem at COLT . The problem now has a cash prize (up to $500) associated with it—see the presentation for details. The write-up for colt .
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