hunch_net hunch_net-2009 hunch_net-2009-357 knowledge-graph by maker-knowledge-mining
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Introduction: There are at least 3 summer schools related to machine learning this summer. The first is at University of Chicago June 1-11 organized by Misha Belkin , Partha Niyogi , and Steve Smale . Registration is closed for this one, meaning they met their capacity limit. The format is essentially an extended Tutorial/Workshop. I was particularly interested to see Valiant amongst the speakers. I’m also presenting Saturday June 6, on logarithmic time prediction. Praveen Srinivasan points out the second at Peking University in Beijing, China, July 20-27. This one differs substantially, as it is about vision, machine learning, and their intersection. The deadline for applications is June 10 or 15. This is also another example of the growth of research in China, with active support from NSF . The third one is at Cambridge , England, August 29-September 10. It’s in the MLSS series . Compared to the Chicago one, this one is more about the Bayesian side of ML, alth
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1 There are at least 3 summer schools related to machine learning this summer. [sent-1, score-0.217]
2 The first is at University of Chicago June 1-11 organized by Misha Belkin , Partha Niyogi , and Steve Smale . [sent-2, score-0.118]
3 Registration is closed for this one, meaning they met their capacity limit. [sent-3, score-0.493]
4 I was particularly interested to see Valiant amongst the speakers. [sent-5, score-0.082]
5 I’m also presenting Saturday June 6, on logarithmic time prediction. [sent-6, score-0.334]
6 This one differs substantially, as it is about vision, machine learning, and their intersection. [sent-8, score-0.215]
7 This is also another example of the growth of research in China, with active support from NSF . [sent-10, score-0.371]
8 The third one is at Cambridge , England, August 29-September 10. [sent-11, score-0.197]
9 Compared to the Chicago one, this one is more about the Bayesian side of ML, although effort has been made to create a good cross section of topics. [sent-13, score-0.492]
10 It’s also more focused on tutorials over workshop-style talks. [sent-14, score-0.304]
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Introduction: There are at least 3 summer schools related to machine learning this summer. The first is at University of Chicago June 1-11 organized by Misha Belkin , Partha Niyogi , and Steve Smale . Registration is closed for this one, meaning they met their capacity limit. The format is essentially an extended Tutorial/Workshop. I was particularly interested to see Valiant amongst the speakers. I’m also presenting Saturday June 6, on logarithmic time prediction. Praveen Srinivasan points out the second at Peking University in Beijing, China, July 20-27. This one differs substantially, as it is about vision, machine learning, and their intersection. The deadline for applications is June 10 or 15. This is also another example of the growth of research in China, with active support from NSF . The third one is at Cambridge , England, August 29-September 10. It’s in the MLSS series . Compared to the Chicago one, this one is more about the Bayesian side of ML, alth
2 0.21863142 414 hunch net-2010-10-17-Partha Niyogi has died
Introduction: from brain cancer. I asked Misha who worked with him to write about it. Partha Niyogi, Louis Block Professor in Computer Science and Statistics at the University of Chicago passed away on October 1, 2010, aged 43. I first met Partha Niyogi almost exactly ten years ago when I was a graduate student in math and he had just started as a faculty in Computer Science and Statistics at the University of Chicago. Strangely, we first talked at length due to a somewhat convoluted mathematical argument in a paper on pattern recognition. I asked him some questions about the paper, and, even though the topic was new to him, he had put serious thought into it and we started regular meetings. We made significant progress and developed a line of research stemming initially just from trying to understand that one paper and to simplify one derivation. I think this was typical of Partha, showing both his intellectual curiosity and his intuition for the serendipitous; having a sense and focus fo
3 0.15772936 17 hunch net-2005-02-10-Conferences, Dates, Locations
Introduction: Conference Locate Date COLT Bertinoro, Italy June 27-30 AAAI Pittsburgh, PA, USA July 9-13 UAI Edinburgh, Scotland July 26-29 IJCAI Edinburgh, Scotland July 30 – August 5 ICML Bonn, Germany August 7-11 KDD Chicago, IL, USA August 21-24 The big winner this year is Europe. This is partly a coincidence, and partly due to the general internationalization of science over the last few years. With cuts to basic science in the US and increased hassle for visitors, conferences outside the US become more attractive. Europe and Australia/New Zealand are the immediate winners because they have the science, infrastructure, and english in place. China and India are possible future winners.
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Introduction: IJCAI is running January 6-12 in Hyderabad India rather than a more traditional summer date. (Presumably, this is to avoid melting people in the Indian summer.) The paper deadline(June 23 abstract / June 30 submission) are particularly inconvenient if you attend COLT or ICML . But on the other hand, it’s a good excuse to visit India.
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Introduction: Many different paper deadlines are coming up soon so I made a little reference table. Out of curiosity, I also computed the interval between submission deadline and conference. Conference Location Date Deadline interval COLT Pittsburgh June 22-25 January 21 152 ICML Pittsburgh June 26-28 January 30/February 6 140 UAI MIT July 13-16 March 9/March 16 119 AAAI Boston July 16-20 February 16/21 145 KDD Philadelphia August 23-26 March 3/March 10 166 It looks like the northeastern US is the big winner as far as location this year.
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Introduction: There are at least 3 summer schools related to machine learning this summer. The first is at University of Chicago June 1-11 organized by Misha Belkin , Partha Niyogi , and Steve Smale . Registration is closed for this one, meaning they met their capacity limit. The format is essentially an extended Tutorial/Workshop. I was particularly interested to see Valiant amongst the speakers. I’m also presenting Saturday June 6, on logarithmic time prediction. Praveen Srinivasan points out the second at Peking University in Beijing, China, July 20-27. This one differs substantially, as it is about vision, machine learning, and their intersection. The deadline for applications is June 10 or 15. This is also another example of the growth of research in China, with active support from NSF . The third one is at Cambridge , England, August 29-September 10. It’s in the MLSS series . Compared to the Chicago one, this one is more about the Bayesian side of ML, alth
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Introduction: There are several summer schools related to machine learning. We are running a two week machine learning summer school in Chicago, USA May 16-27. IPAM is running a more focused three week summer school on Intelligent Extraction of Information from Graphs and High Dimensional Data in Los Angeles, USA July 11-29. A broad one-week school on analysis of patterns will be held in Erice, Italy, Oct. 28-Nov 6.
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Introduction: There will be two machine learning summer schools in 2006. One is in Canberra, Australia from February 6 to February 17 (Aussie summer). The webpage is fully ‘live’ so you should actively consider it now. The other is in Taipei, Taiwan from July 24 to August 4. This one is still in the planning phase, but that should be settled soon. Attending an MLSS is probably the quickest and easiest way to bootstrap yourself into a reasonable initial understanding of the field of machine learning.
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Introduction: We just finished the Chicago 2005 Machine Learning Summer School . The school was 2 weeks long with about 130 (or 140 counting the speakers) participants. For perspective, this is perhaps the largest graduate level machine learning class I am aware of anywhere and anytime (previous MLSS s have been close). Overall, it seemed to go well, although the students are the real authority on this. For those who missed it, DVDs will be available from our Slovenian friends. Email Mrs Spela Sitar of the Jozsef Stefan Institute for details. The following are some notes for future planning and those interested. Good Decisions Acquiring the larger-than-necessary “Assembly Hall” at International House . Our attendance came in well above our expectations, so this was a critical early decision that made a huge difference. The invited speakers were key. They made a huge difference in the quality of the content. Delegating early and often was important. One key difficulty here
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Introduction: Geoff Gordon points out AIStats 2011 in Ft. Lauderdale, Florida. The call for papers is now out, due Nov. 1. The plan is to experiment with the review process to encourage quality in several ways. I expect to submit a paper and would encourage others with good research to do likewise.
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Introduction: There are at least 3 summer schools related to machine learning this summer. The first is at University of Chicago June 1-11 organized by Misha Belkin , Partha Niyogi , and Steve Smale . Registration is closed for this one, meaning they met their capacity limit. The format is essentially an extended Tutorial/Workshop. I was particularly interested to see Valiant amongst the speakers. I’m also presenting Saturday June 6, on logarithmic time prediction. Praveen Srinivasan points out the second at Peking University in Beijing, China, July 20-27. This one differs substantially, as it is about vision, machine learning, and their intersection. The deadline for applications is June 10 or 15. This is also another example of the growth of research in China, with active support from NSF . The third one is at Cambridge , England, August 29-September 10. It’s in the MLSS series . Compared to the Chicago one, this one is more about the Bayesian side of ML, alth
3 0.69297057 28 hunch net-2005-02-25-Problem: Online Learning
Introduction: Despite my best intentions, this is not a fully specified problem, but rather a research direction. Competitive online learning is one of the more compelling pieces of learning theory because typical statements of the form “this algorithm will perform almost as well as a large set of other algorithms” rely only on fully-observable quantities, and are therefore applicable in many situations. Examples include Winnow , Weighted Majority , and Binomial Weighting . Algorithms with this property haven’t taken over the world yet. Here might be some reasons: Lack of caring . Many people working on learning theory don’t care about particular applications much. This means constants in the algorithm are not optimized, usable code is often not produced, and empirical studies aren’t done. Inefficiency . Viewed from the perspective of other learning algorithms, online learning is terribly inefficient. It requires that every hypothesis (called an expert in the online learning set
4 0.56067026 1 hunch net-2005-01-19-Why I decided to run a weblog.
Introduction: I have decided to run a weblog on machine learning and learning theory research. Here are some reasons: 1) Weblogs enable new functionality: Public comment on papers. No mechanism for this exists at conferences and most journals. I have encountered it once for a science paper. Some communities have mailing lists supporting this, but not machine learning or learning theory. I have often read papers and found myself wishing there was some method to consider other’s questions and read the replies. Conference shortlists. One of the most common conversations at a conference is “what did you find interesting?” There is no explicit mechanism for sharing this information at conferences, and it’s easy to imagine that it would be handy to do so. Evaluation and comment on research directions. Papers are almost exclusively about new research, rather than evaluation (and consideration) of research directions. This last role is satisfied by funding agencies to some extent, but
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