hunch_net hunch_net-2007 hunch_net-2007-232 knowledge-graph by maker-knowledge-mining
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Introduction: To commemorate the Twenty Fourth Annual International Conference on Machine Learning (ICML-07), the FOX Network has decided to launch a new spin-off series in prime time. Through unofficial sources, I have obtained the story arc for the first season, which appears frighteningly realistic.
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1 To commemorate the Twenty Fourth Annual International Conference on Machine Learning (ICML-07), the FOX Network has decided to launch a new spin-off series in prime time. [sent-1, score-1.124]
2 Through unofficial sources, I have obtained the story arc for the first season, which appears frighteningly realistic. [sent-2, score-1.077]
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same-blog 1 1.0000001 232 hunch net-2007-02-11-24
Introduction: To commemorate the Twenty Fourth Annual International Conference on Machine Learning (ICML-07), the FOX Network has decided to launch a new spin-off series in prime time. Through unofficial sources, I have obtained the story arc for the first season, which appears frighteningly realistic.
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Introduction: The Second Annual Reinforcement Learning Competition is about to get started. The aim of the competition is to facilitate direct comparisons between various learning methods on important and realistic domains. This year’s event will feature well-known benchmark domains as well as more challenging problems of real-world complexity, such as helicopter control and robot soccer keepaway. The competition begins on November 1st, 2007 when training software is released. Results must be submitted by July 1st, 2008. The competition will culminate in an event at ICML-08 in Helsinki, Finland, at which the winners will be announced. For more information, visit the competition website.
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Introduction: It’s conference season, and smell of budding papers is in the air. IJCAI 2005 , January 21 COLT 2005 , February 2 KDD 2005 , February 18 ICML 2005 , March 8 UAI 2005 , March 16 AAAI 2005 , March 18
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Introduction: I’m visiting Beijing for the Pao-Lu Hsu Statistics Conference on Machine Learning. I had several discussions about the state of Chinese research. Given the large population and economy, you might expect substantial research—more than has been observed at international conferences. The fundamental problem seems to be the Cultural Revolution which lobotimized higher education, and the research associated with it. There has been a process of slow recovery since then, which has begun to be felt in the research world via increased participation in international conferences and (now) conferences in China. The amount of effort going into construction in Beijing is very impressive—people are literally building a skyscraper at night outside the window of the hotel I’m staying at (and this is not unusual). If a small fraction of this effort is later focused onto supporting research, the effect could be very substantial. General growth in China’s research portfolio should be expecte
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Introduction: Registration for COLT 2007 is now open. The conference will take place on 13-15 June, 2007, in San Diego, California, as part of the 2007 Federated Computing Research Conference (FCRC), which includes STOC, Complexity, and EC. The website for COLT: http://www.learningtheory.org/colt2007/index.html The early registration deadline is May 11, and the cutoff date for discounted hotel rates is May 9. Before registering, take note that the fees are substantially lower for members of ACM and/or SIGACT than for nonmembers. If you’ve been contemplating joining either of these two societies (annual dues: $99 for ACM, $18 for SIGACT), now would be a good time!
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Introduction: To commemorate the Twenty Fourth Annual International Conference on Machine Learning (ICML-07), the FOX Network has decided to launch a new spin-off series in prime time. Through unofficial sources, I have obtained the story arc for the first season, which appears frighteningly realistic.
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Introduction: Some of the “sister conference” presentations at AAAI have been great. Roughly speaking, the conference organizers asked other conference organizers to come give a summary of their conference. Many different AI-related conferences accepted. The presenters typically discuss some of the background and goals of the conference then mention the results from a few papers they liked. This is great because it provides a mechanism to get a digested overview of the work of several thousand researchers—something which is simply available nowhere else. Based on these presentations, it looks like there is a significant component of (and opportunity for) applied machine learning in AIIDE , IUI , and ACL . There was also some discussion of having a super-colocation event similar to FCRC , but centered on AI & Learning. This seems like a fine idea. The field is fractured across so many different conferences that the mixing of a supercolocation seems likely helpful for research.
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Introduction: As part of a PASCAL project, the Slovenians have been filming various machine learning events and placing them on the web here . This includes, for example, the Chicago 2005 Machine Learning Summer School as well as a number of other summer schools, workshops, and conferences. There are some significant caveats here—for example, I can’t access it from Linux. Based upon the webserver logs, I expect that is a problem for most people—Computer scientists are particularly nonstandard in their choice of computing platform. Nevertheless, the core idea here is excellent and details of compatibility can be fixed later. With modern technology toys, there is no fundamental reason why the process of announcing new work at a conference should happen only once and only for the people who could make it to that room in that conference. The problems solved include: The multitrack vs. single-track debate. (“Sometimes the single track doesn’t interest me” vs. “When it’s multitrack I mis
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Introduction: (update: cross-posted on CACM ) For the first time in several years, ICML 2010 did not have videolectures attending. Luckily, the tutorial on exploration and learning which Alina and I put together can be viewed , since we also presented at KDD 2010 , which included videolecture support. ICML didn’t cover the cost of a videolecture, because PASCAL didn’t provide a grant for it this year. On the other hand, KDD covered it out of registration costs. The cost of videolectures isn’t cheap. For a workshop the baseline quote we have is 270 euro per hour, plus a similar cost for the cameraman’s travel and accomodation. This can be reduced substantially by having a volunteer with a camera handle the cameraman duties, uploading the video and slides to be processed for a quoted 216 euro per hour. Youtube is the most predominant free video site with a cost of $0, but it turns out to be a poor alternative. 15 minute upload limits do not match typical talk lengths.
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Introduction: To commemorate the Twenty Fourth Annual International Conference on Machine Learning (ICML-07), the FOX Network has decided to launch a new spin-off series in prime time. Through unofficial sources, I have obtained the story arc for the first season, which appears frighteningly realistic.
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Introduction: Just about nothing could keep me from attending ICML , except for Dora who arrived on Monday. Consequently, I have only secondhand reports that the conference is going well. For those who are remote (like me) or after the conference (like everyone), Mark Reid has setup the ICML discussion site where you can comment on any paper or subscribe to papers. Authors are automatically subscribed to their own papers, so it should be possible to have a discussion significantly after the fact, as people desire. We also conducted a survey before the conference and have the survey results now. This can be compared with the ICML 2010 survey results . Looking at the comparable questions, we can sometimes order the answers to have scores ranging from 0 to 3 or 0 to 4 with 3 or 4 being best and 0 worst, then compute the average difference between 2012 and 2010. Glancing through them, I see: Most people found the papers they reviewed a good fit for their expertise (-.037 w.r.t 20
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Introduction: This post is about a trick that I learned from Dale Schuurmans which has been repeatedly useful for me over time. The basic trick has to do with importance weighting for monte carlo integration. Consider the problem of finding: N = E x ~ D f(x) given samples from D and knowledge of f . Often, we don’t have samples from D available. Instead, we must make do with samples from some other distribution Q . In that case, we can still often solve the problem, as long as Q(x) isn’t 0 when D(x) is nonzero, using the importance weighting formula: E x ~ Q f(x) D(x)/Q(x) A basic question is: How many samples from Q are required in order to estimate N to some precision? In general the convergence rate is not bounded, because f(x) D(x)/Q(x) is not bounded given the assumptions. Nevertheless, there is one special value Q(x) = f(x) D(x) / N where the sample complexity turns out to be 1 , which is typically substantially better than the sample complexity of the orig
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