hunch_net hunch_net-2006 hunch_net-2006-172 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: In 2001, the “ Journal of Machine Learning Research ” was created in reaction to unadaptive publisher policies at MLJ . Essentially, with the creation of the internet, the bottleneck in publishing research shifted from publishing to research. The declaration of independence accompanying this move expresses the reasons why in greater detail. MLJ has strongly changed its policy in reaction to this. In particular, there is no longer an assignment of copyright to the publisher (*), and MLJ regularly sponsors many student “best paper awards” across several conferences with cash prizes. This is an advantage of MLJ over JMLR: MLJ can afford to sponsor cash prizes for the machine learning community. The remaining disadvantage is that reading papers in MLJ sometimes requires searching for the author’s website where the free version is available. In contrast, JMLR articles are freely available to everyone off the JMLR website. Whether or not this disadvantage cancels the advantage i
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1 In 2001, the “ Journal of Machine Learning Research ” was created in reaction to unadaptive publisher policies at MLJ . [sent-1, score-0.438]
2 Essentially, with the creation of the internet, the bottleneck in publishing research shifted from publishing to research. [sent-2, score-0.542]
3 The declaration of independence accompanying this move expresses the reasons why in greater detail. [sent-3, score-0.307]
4 MLJ has strongly changed its policy in reaction to this. [sent-4, score-0.186]
5 In particular, there is no longer an assignment of copyright to the publisher (*), and MLJ regularly sponsors many student “best paper awards” across several conferences with cash prizes. [sent-5, score-0.673]
6 This is an advantage of MLJ over JMLR: MLJ can afford to sponsor cash prizes for the machine learning community. [sent-6, score-0.5]
7 The remaining disadvantage is that reading papers in MLJ sometimes requires searching for the author’s website where the free version is available. [sent-7, score-0.387]
8 In contrast, JMLR articles are freely available to everyone off the JMLR website. [sent-8, score-0.159]
9 Whether or not this disadvantage cancels the advantage is debatable, but essentially no one working on machine learning argues with the following: the changes brought by the creation of JMLR have been positive for the general machine learning community. [sent-9, score-0.551]
10 This model can and should be emulated in other areas of research where publishers are not behaving in a sufficiently constructive manner. [sent-10, score-0.222]
11 Doing so requires two vital ingredients: a consensus of leaders to support a new journal and the willigness to spend the time and effort setting it up. [sent-11, score-0.535]
12 Presumably, some lessons on how to do this have been learned by the editors of JMLR and they are willing to share it. [sent-12, score-0.238]
13 (*) Back in the day, it was typical to be forced to sign over all rights to your journal paper, then ignore this and place it on your homepage. [sent-13, score-0.476]
14 The natural act of placing your paper on your webpage is no longer illegal. [sent-14, score-0.392]
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same-blog 1 0.99999976 172 hunch net-2006-04-14-JMLR is a success
Introduction: In 2001, the “ Journal of Machine Learning Research ” was created in reaction to unadaptive publisher policies at MLJ . Essentially, with the creation of the internet, the bottleneck in publishing research shifted from publishing to research. The declaration of independence accompanying this move expresses the reasons why in greater detail. MLJ has strongly changed its policy in reaction to this. In particular, there is no longer an assignment of copyright to the publisher (*), and MLJ regularly sponsors many student “best paper awards” across several conferences with cash prizes. This is an advantage of MLJ over JMLR: MLJ can afford to sponsor cash prizes for the machine learning community. The remaining disadvantage is that reading papers in MLJ sometimes requires searching for the author’s website where the free version is available. In contrast, JMLR articles are freely available to everyone off the JMLR website. Whether or not this disadvantage cancels the advantage i
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Introduction: The Journal of Machine Learning Gossip has some fine satire about learning research. In particular, the guides are amusing and remarkably true. As in all things, it’s easy to criticize the way things are and harder to make them better.
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Introduction: Essentially everyone who writes research papers suffers rejections. They always sting immediately, but upon further reflection many of these rejections come to seem reasonable. Maybe the equations had too many typos or maybe the topic just isn’t as important as was originally thought. A few rejections do not come to seem acceptable, and these form the basis of reviewing horror stories, a great material for conversations. I’ve decided to share three of mine, now all safely a bit distant in the past. Prediction Theory for Classification Tutorial . This is a tutorial about tight sample complexity bounds for classification that I submitted to JMLR . The first decision I heard was a reject which appeared quite unjust to me—for example one of the reviewers appeared to claim that all the content was in standard statistics books. Upon further inquiry, several citations were given, none of which actually covered the content. Later, I was shocked to hear the paper was accepted. App
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Introduction: In 2001, the “ Journal of Machine Learning Research ” was created in reaction to unadaptive publisher policies at MLJ . Essentially, with the creation of the internet, the bottleneck in publishing research shifted from publishing to research. The declaration of independence accompanying this move expresses the reasons why in greater detail. MLJ has strongly changed its policy in reaction to this. In particular, there is no longer an assignment of copyright to the publisher (*), and MLJ regularly sponsors many student “best paper awards” across several conferences with cash prizes. This is an advantage of MLJ over JMLR: MLJ can afford to sponsor cash prizes for the machine learning community. The remaining disadvantage is that reading papers in MLJ sometimes requires searching for the author’s website where the free version is available. In contrast, JMLR articles are freely available to everyone off the JMLR website. Whether or not this disadvantage cancels the advantage i
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Introduction: 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: (Several) Reviewers are overloaded, boosting the noise in decision making. ( Yann ) A new system should run with as little built-in delay and friction to the process of research as possible. ( Hanna Wallach (updated)) Double-blind review is particularly impor
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Introduction: Dear Fellow Machine Learners, For the past year or so I have become increasingly frustrated with the peer review system in our field. I constantly get asked to review papers in which I have no interest. At the same time, as an action editor in JMLR, I constantly have to harass people to review papers. When I send papers to conferences and to journals I often get rejected with reviews that, at least in my mind, make no sense. Finally, I have a very hard time keeping up with the best new work, because I don’t know where to look for it… I decided to try an do something to improve the situation. I started a new web site, which I decided to call “The machine learning forum” the URL is http://themachinelearningforum.org The main idea behind this web site is to remove anonymity from the review process. In this site, all opinions are attributed to the actual person that expressed them. I expect that this will improve the quality of the reviews. An obvious other effect is that there wil
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Introduction: In 2001, the “ Journal of Machine Learning Research ” was created in reaction to unadaptive publisher policies at MLJ . Essentially, with the creation of the internet, the bottleneck in publishing research shifted from publishing to research. The declaration of independence accompanying this move expresses the reasons why in greater detail. MLJ has strongly changed its policy in reaction to this. In particular, there is no longer an assignment of copyright to the publisher (*), and MLJ regularly sponsors many student “best paper awards” across several conferences with cash prizes. This is an advantage of MLJ over JMLR: MLJ can afford to sponsor cash prizes for the machine learning community. The remaining disadvantage is that reading papers in MLJ sometimes requires searching for the author’s website where the free version is available. In contrast, JMLR articles are freely available to everyone off the JMLR website. Whether or not this disadvantage cancels the advantage i
2 0.99713224 247 hunch net-2007-06-14-Interesting Papers at COLT 2007
Introduction: Here are two papers that seem particularly interesting at this year’s COLT. Gilles Blanchard and François Fleuret , Occam’s Hammer . When we are interested in very tight bounds on the true error rate of a classifier, it is tempting to use a PAC-Bayes bound which can (empirically) be quite tight . A disadvantage of the PAC-Bayes bound is that it applies to a classifier which is randomized over a set of base classifiers rather than a single classifier. This paper shows that a similar bound can be proved which holds for a single classifier drawn from the set. The ability to safely use a single classifier is very nice. This technique applies generically to any base bound, so it has other applications covered in the paper. Adam Tauman Kalai . Learning Nested Halfspaces and Uphill Decision Trees . Classification PAC-learning, where you prove that any problem amongst some set is polytime learnable with respect to any distribution over the input X is extraordinarily ch
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Introduction: In the regression vs classification debate , I’m adding a new “pro” to classification. It seems there are computational shortcuts available for classification which simply aren’t available for regression. This arises in several situations. In active learning it is sometimes possible to find an e error classifier with just log(e) labeled samples. Only much more modest improvements appear to be achievable for squared loss regression. The essential reason is that the loss function on many examples is flat with respect to large variations in the parameter spaces of a learned classifier, which implies that many of these classifiers do not need to be considered. In contrast, for squared loss regression, most substantial variations in the parameter space influence the loss at most points. In budgeted learning, where there is either a computational time constraint or a feature cost constraint, a classifier can sometimes be learned to very high accuracy under the constraints
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