hunch_net hunch_net-2005 hunch_net-2005-11 knowledge-graph by maker-knowledge-mining
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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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same-blog 1 0.99999994 11 hunch net-2005-02-02-Paper Deadlines
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
2 0.36708033 387 hunch net-2010-01-19-Deadline Season, 2010
Introduction: Many conference deadlines are coming soon. Deadline Double Blind / Author Feedback Time/Place ICML January 18((workshops) / February 1 (Papers) / February 13 (Tutorials) Y/Y Haifa, Israel, June 21-25 KDD February 1(Workshops) / February 2&5 (Papers) / February 26 (Tutorials & Panels)) / April 17 (Demos) N/S Washington DC, July 25-28 COLT January 18 (Workshops) / February 19 (Papers) N/S Haifa, Israel, June 25-29 UAI March 11 (Papers) N?/Y Catalina Island, California, July 8-11 ICML continues to experiment with the reviewing process, although perhaps less so than last year. The S “sort-of” for COLT is because author feedback occurs only after decisions are made. KDD is notable for being the most comprehensive in terms of {Tutorials, Workshops, Challenges, Panels, Papers (two tracks), Demos}. The S for KDD is because there is sometimes author feedback at the decision of the SPC. The (past) January 18 de
3 0.34763205 145 hunch net-2005-12-29-Deadline Season
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.
4 0.34658018 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences
Introduction: It’s conference season once again. Conference Due? When? Where? double blind? author feedback? Workshops? AAAI February 1/6 (and 27) July 22-26 Vancouver, British Columbia Yes Yes Done UAI February 28/March 2 July 19-22 Vancouver, British Columbia No No No COLT January 16 June 13-15 San Diego, California (with FCRC ) No No No ICML February 7/9 June 20-24 Corvallis, Oregon Yes Yes February 16 KDD February 23/28 August 12-15 San Jose, California Yes No? February 28 The geowinner this year is the west coast of North America. Last year ‘s geowinner was the Northeastern US, and the year before it was mostly Europe. It’s notable how tightly the conferences cluster, even when they don’t colocate.
5 0.31798142 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
Introduction: Machine learning always welcomes the new year with paper deadlines for summer conferences. This year, we have: Conference Paper Deadline When/Where Double blind? Author Feedback? Notes ICML February 1 June 28-July 2, Bellevue, Washington, USA Y Y Weak colocation with ACL COLT February 11 July 9-July 11, Budapest, Hungary N N colocated with FOCM KDD February 11/18 August 21-24, San Diego, California, USA N N UAI March 18 July 14-17, Barcelona, Spain Y N The larger conferences are on the west coast in the United States, while the smaller ones are in Europe.
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8 0.16342771 494 hunch net-2014-03-11-The New York ML Symposium, take 2
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13 0.12038201 184 hunch net-2006-06-15-IJCAI is out of season
14 0.12025191 403 hunch net-2010-07-18-ICML & COLT 2010
15 0.11514771 66 hunch net-2005-05-03-Conference attendance is mandatory
16 0.10459477 489 hunch net-2013-09-20-No NY ML Symposium in 2013, and some good news
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19 0.098125309 457 hunch net-2012-02-29-Key Scientific Challenges and the Franklin Symposium
20 0.09732499 453 hunch net-2012-01-28-Why COLT?
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same-blog 1 0.98721755 11 hunch net-2005-02-02-Paper Deadlines
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
2 0.80579066 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences
Introduction: It’s conference season once again. Conference Due? When? Where? double blind? author feedback? Workshops? AAAI February 1/6 (and 27) July 22-26 Vancouver, British Columbia Yes Yes Done UAI February 28/March 2 July 19-22 Vancouver, British Columbia No No No COLT January 16 June 13-15 San Diego, California (with FCRC ) No No No ICML February 7/9 June 20-24 Corvallis, Oregon Yes Yes February 16 KDD February 23/28 August 12-15 San Jose, California Yes No? February 28 The geowinner this year is the west coast of North America. Last year ‘s geowinner was the Northeastern US, and the year before it was mostly Europe. It’s notable how tightly the conferences cluster, even when they don’t colocate.
3 0.80061221 145 hunch net-2005-12-29-Deadline Season
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.
4 0.78041649 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
Introduction: Machine learning always welcomes the new year with paper deadlines for summer conferences. This year, we have: Conference Paper Deadline When/Where Double blind? Author Feedback? Notes ICML February 1 June 28-July 2, Bellevue, Washington, USA Y Y Weak colocation with ACL COLT February 11 July 9-July 11, Budapest, Hungary N N colocated with FOCM KDD February 11/18 August 21-24, San Diego, California, USA N N UAI March 18 July 14-17, Barcelona, Spain Y N The larger conferences are on the west coast in the United States, while the smaller ones are in Europe.
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Introduction: Many conference deadlines are coming soon. Deadline Double Blind / Author Feedback Time/Place ICML January 18((workshops) / February 1 (Papers) / February 13 (Tutorials) Y/Y Haifa, Israel, June 21-25 KDD February 1(Workshops) / February 2&5 (Papers) / February 26 (Tutorials & Panels)) / April 17 (Demos) N/S Washington DC, July 25-28 COLT January 18 (Workshops) / February 19 (Papers) N/S Haifa, Israel, June 25-29 UAI March 11 (Papers) N?/Y Catalina Island, California, July 8-11 ICML continues to experiment with the reviewing process, although perhaps less so than last year. The S “sort-of” for COLT is because author feedback occurs only after decisions are made. KDD is notable for being the most comprehensive in terms of {Tutorials, Workshops, Challenges, Panels, Papers (two tracks), Demos}. The S for KDD is because there is sometimes author feedback at the decision of the SPC. The (past) January 18 de
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same-blog 1 0.96427852 11 hunch net-2005-02-02-Paper Deadlines
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
2 0.46777934 397 hunch net-2010-05-02-What’s the difference between gambling and rewarding good prediction?
Introduction: After a major financial crisis , there is much discussion about how finance has become a casino gambling with other’s money, keeping the winnings, and walking away when the money is lost. When thinking about financial reform, all the many losers in the above scenario are apt to take the view that this activity should be completely, or nearly completely curtailed. But, a more thoughtful view is that sometimes there is a real sense in which there are right and wrong decisions, and we as a society would really prefer that the people most likely to make right decisions are making them. A crucial question then is: “What is the difference between gambling and rewarding good prediction?” We discussed this before the financial crisis . The cheat-sheet sketch is that the online learning against an adversary problem, algorithm, and theorems, provide a good mathematical model for thinking about this question. What I would like to do here is map this onto various types of financial t
3 0.38168514 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: 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: Hal , Daniel , and I have been working on the algorithm Searn for structured prediction. This was just conditionally accepted and then rejected from ICML, and we were quite surprised. By any reasonable criteria, it seems this is an interesting algorithm. Prediction Performance: Searn performed better than any other algorithm on all the problems we tested against using the same feature set. This is true even using the numbers reported by authors in their papers. Theoretical underpinning. Searn is a reduction which comes with a reduction guarantee: the good performance on a base classifiers implies good performance for the overall system. No other theorem of this type has been made for other structured prediction algorithms, as far as we know. Speed. Searn has no problem handling much larger datasets than other algorithms we tested against. Simplicity. Given code for a binary classifier and a problem-specific search algorithm, only a few tens of lines are necessary to
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