hunch_net hunch_net-2012 hunch_net-2012-452 knowledge-graph by maker-knowledge-mining
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Introduction: Here’s a quick reference for summer ML-related conferences sorted by due date: Conference Due date Location Reviewing KDD Feb 10 August 12-16, Beijing, China Single Blind COLT Feb 14 June 25-June 27, Edinburgh, Scotland Single Blind? (historically) ICML Feb 24 June 26-July 1, Edinburgh, Scotland Double Blind, author response, zero SPOF UAI March 30 August 15-17, Catalina Islands, California Double Blind, author response Geographically, this is greatly dispersed and the UAI/KDD conflict is unfortunate. Machine Learning conferences are triannual now, between NIPS , AIStat , and ICML . This has not always been the case: the academic default is annual summer conferences, then NIPS started with a December conference, and now AIStat has grown into an April conference. However, the first claim is not quite correct. NIPS and AIStat have few competing venues while ICML implicitly competes with many other conf
sentIndex sentText sentNum sentScore
1 NIPS and AIStat have few competing venues while ICML implicitly competes with many other conferences accepting machine learning related papers. [sent-6, score-0.326]
2 COLT was historically a conference for learning-interested Computer Science theory people. [sent-8, score-0.349]
3 A significant subset of COLT papers could easily be published at ICML instead. [sent-10, score-0.345]
4 ICML now has a significant theory community, including many pure theory papers and significant overlap with COLT attendees. [sent-11, score-0.689]
5 Good candidates for an ICML submission are learning theory papers motivated by real machine learning problems (example: the agnostic active learning paper) or which propose and analyze new plausibly useful algorithms (example: the adaptive gradient papers). [sent-12, score-0.561]
6 The other is that ICML is committed to fair reviewing—papers are double blind so reviewers are not forced to take into account the author identity. [sent-18, score-0.57]
7 Plenty of people will argue that author names don’t matter to them, but I’ve personally seen several cases as a reviewer where author identity affected the decision, typically towards favoring insiders or bigwigs at theory conferences as common sense would suggest. [sent-19, score-0.56]
8 The double blind aspect of ICML reviewing is an open invitation to outsiders to submit to ICML. [sent-20, score-0.485]
9 Many UAI papers could easily go to ICML because they are explicitly about machine learning or connections with machine learning. [sent-21, score-0.596]
10 For example, pure prediction market s are a stretch for ICML, but connections between machine learning and prediction markets, which seem to come up in multiple ways, are a good fit. [sent-22, score-0.463]
11 ICML provides a significantly larger potential audience and, due to it’s size, tends to be more diverse. [sent-31, score-0.284]
12 KDD is a large conference (a bit larger than ICML by attendance) which, as I understand it, initially started from the viewpoint of database people trying to do interesting things with the data they had. [sent-32, score-0.286]
13 Significant parts of the academic track are about machine learning technology and could have been submitted to ICML instead. [sent-34, score-0.342]
14 I was impressed by the double robust sampling work and the out of core learning paper is cool. [sent-35, score-0.325]
15 KDD doesn’t do double blind review, which was discussed above. [sent-39, score-0.386]
16 To me, a more significant drawback of KDD is the ACM paywall . [sent-40, score-0.285]
17 Depending on many circumstances, ICML might be a good candidate for a place to send a paper on a new empirically useful piece of machine learning technology. [sent-52, score-0.321]
18 Machine Learning has grown radically and gone industrial over the last decade, providing plenty of motivation for a conference on developing new core machine learning technology. [sent-54, score-0.477]
19 In most cases, the best place to send a paper is to the conference where it will be most appreciated. [sent-56, score-0.318]
20 So, when the choice is unclear, sending the paper to a conference designed simultaneously for fair high quality reviewing and broad distribution of your work is a good call as it provides the most meaningful acceptance. [sent-58, score-0.482]
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