hunch_net hunch_net-2007 hunch_net-2007-246 knowledge-graph by maker-knowledge-mining
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Introduction: If you have been disappointed by the lack of a post for the last month, consider contributing your own (I’ve been busy+uninspired). Also, keep in mind that there is a community of machine learning blogs (see the sidebar).
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same-blog 1 1.0 246 hunch net-2007-06-13-Not Posting
Introduction: If you have been disappointed by the lack of a post for the last month, consider contributing your own (I’ve been busy+uninspired). Also, keep in mind that there is a community of machine learning blogs (see the sidebar).
2 0.11797123 225 hunch net-2007-01-02-Retrospective
Introduction: It’s been almost two years since this blog began. In that time, I’ve learned enough to shift my expectations in several ways. Initially, the idea was for a general purpose ML blog where different people could contribute posts. What has actually happened is most posts come from me, with a few guest posts that I greatly value. There are a few reasons I see for this. Overload . A couple years ago, I had not fully appreciated just how busy life gets for a researcher. Making a post is not simply a matter of getting to it, but rather of prioritizing between {writing a grant, finishing an overdue review, writing a paper, teaching a class, writing a program, etc…}. This is a substantial transition away from what life as a graduate student is like. At some point the question is not “when will I get to it?” but rather “will I get to it?” and the answer starts to become “no” most of the time. Feedback failure . This blog currently receives about 3K unique visitors per day from
3 0.11527989 15 hunch net-2005-02-08-Some Links
Introduction: Yaroslav Bulatov collects some links to other technical blogs.
4 0.072171785 354 hunch net-2009-05-17-Server Update
Introduction: The hunch.net server has been updated. I’ve taken the opportunity to upgrade the version of wordpress which caused cascading changes. Old threaded comments are now flattened. The system we used to use ( Brian’s threaded comments ) appears incompatible with the new threading system built into wordpress. I haven’t yet figured out a workaround. I setup a feedburner account . I added an RSS aggregator for both Machine Learning and other research blogs that I like to follow. This is something that I’ve wanted to do for awhile. Many other minor changes in font and format, with some help from Alina . If you have any suggestions for site tweaks, please speak up.
5 0.069161452 39 hunch net-2005-03-10-Breaking Abstractions
Introduction: Sam Roweis ‘s comment reminds me of a more general issue that comes up in doing research: abstractions always break. Real number’s aren’t. Most real numbers can not be represented with any machine. One implication of this is that many real-number based algorithms have difficulties when implemented with floating point numbers. The box on your desk is not a turing machine. A turing machine can compute anything computable, given sufficient time. A typical computer fails terribly when the state required for the computation exceeds some limit. Nash equilibria aren’t equilibria. This comes up when trying to predict human behavior based on the result of the equilibria computation. Often, it doesn’t work. The probability isn’t. Probability is an abstraction expressing either our lack of knowledge (the Bayesian viewpoint) or fundamental randomization (the frequentist viewpoint). From the frequentist viewpoint the lack of knowledge typically precludes actually knowing the fu
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20 0.045089878 280 hunch net-2007-12-20-Cool and Interesting things at NIPS, take three
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simIndex simValue blogId blogTitle
same-blog 1 0.93077564 246 hunch net-2007-06-13-Not Posting
Introduction: If you have been disappointed by the lack of a post for the last month, consider contributing your own (I’ve been busy+uninspired). Also, keep in mind that there is a community of machine learning blogs (see the sidebar).
2 0.51719832 354 hunch net-2009-05-17-Server Update
Introduction: The hunch.net server has been updated. I’ve taken the opportunity to upgrade the version of wordpress which caused cascading changes. Old threaded comments are now flattened. The system we used to use ( Brian’s threaded comments ) appears incompatible with the new threading system built into wordpress. I haven’t yet figured out a workaround. I setup a feedburner account . I added an RSS aggregator for both Machine Learning and other research blogs that I like to follow. This is something that I’ve wanted to do for awhile. Many other minor changes in font and format, with some help from Alina . If you have any suggestions for site tweaks, please speak up.
3 0.49307626 107 hunch net-2005-09-05-Site Update
Introduction: I tweaked the site in a number of ways today, including: Updating to WordPress 1.5. Installing and heavily tweaking the Geekniche theme. Update: I switched back to a tweaked version of the old theme. Adding the Customizable Post Listings plugin. Installing the StatTraq plugin. Updating some of the links. I particularly recommend looking at the computer research policy blog. Adding threaded comments . This doesn’t thread old comments obviously, but the extra structure may be helpful for new ones. Overall, I think this is an improvement, and it addresses a few of my earlier problems . If you have any difficulties or anything seems “not quite right”, please speak up. A few other tweaks to the site may happen in the near future.
4 0.47694132 151 hunch net-2006-01-25-1 year
Introduction: At the one year (+5 days) anniversary, the natural question is: “Was it helpful for research?” Answer: Yes, and so it shall continue. Some evidence is provided by noticing that I am about a factor of 2 more overloaded with paper ideas than I’ve ever previously been. It is always hard to estimate counterfactual worlds, but I expect that this is also a factor of 2 more than “What if I had not started the blog?” As for “Why?”, there seem to be two primary effects. A blog is a mechanism for connecting with people who either think like you or are interested in the same problems. This allows for concentration of thinking which is very helpful in solving problems. The process of stating things you don’t understand publicly is very helpful in understanding them. Sometimes you are simply forced to express them in a way which aids understanding. Sometimes someone else says something which helps. And sometimes you discover that someone else has already solved the problem. The
5 0.47165972 225 hunch net-2007-01-02-Retrospective
Introduction: It’s been almost two years since this blog began. In that time, I’ve learned enough to shift my expectations in several ways. Initially, the idea was for a general purpose ML blog where different people could contribute posts. What has actually happened is most posts come from me, with a few guest posts that I greatly value. There are a few reasons I see for this. Overload . A couple years ago, I had not fully appreciated just how busy life gets for a researcher. Making a post is not simply a matter of getting to it, but rather of prioritizing between {writing a grant, finishing an overdue review, writing a paper, teaching a class, writing a program, etc…}. This is a substantial transition away from what life as a graduate student is like. At some point the question is not “when will I get to it?” but rather “will I get to it?” and the answer starts to become “no” most of the time. Feedback failure . This blog currently receives about 3K unique visitors per day from
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20 0.33041364 280 hunch net-2007-12-20-Cool and Interesting things at NIPS, take three
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Introduction: Hal Daume has started the NLPers blog to discuss learning for language problems.
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Introduction: If you have been disappointed by the lack of a post for the last month, consider contributing your own (I’ve been busy+uninspired). Also, keep in mind that there is a community of machine learning blogs (see the sidebar).
3 1.0 418 hunch net-2010-12-02-Traffic Prediction Problem
Introduction: Slashdot points out the Traffic Prediction Challenge which looks pretty fun. The temporal aspect seems to be very common in many real-world problems and somewhat understudied.
4 0.99896955 274 hunch net-2007-11-28-Computational Consequences of Classification
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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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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