hunch_net hunch_net-2007 hunch_net-2007-270 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Perhaps the biggest CS prize for research is the Turing Award , which has a $0.25M cash prize associated with it. It appears none of the prizes so far have been for anything like machine learning (the closest are perhaps database awards). In CS theory, there is the Gödel Prize which is smaller and newer, offering a $5K prize along and perhaps (more importantly) recognition. One such award has been given for Machine Learning, to Robert Schapire and Yoav Freund for Adaboost. In Machine Learning, there seems to be no equivalent of these sorts of prizes. There are several plausible reasons for this: There is no coherent community. People drift in and out of the central conferences all the time. Most of the author names from 10 years ago do not occur in the conferences of today. In addition, the entire subject area is fairly new. There are at least a core group of people who have stayed around. Machine Learning work doesn’t last Almost every paper is fo
sentIndex sentText sentNum sentScore
1 Perhaps the biggest CS prize for research is the Turing Award , which has a $0. [sent-1, score-0.328]
2 It appears none of the prizes so far have been for anything like machine learning (the closest are perhaps database awards). [sent-3, score-0.431]
3 In CS theory, there is the Gödel Prize which is smaller and newer, offering a $5K prize along and perhaps (more importantly) recognition. [sent-4, score-0.463]
4 One such award has been given for Machine Learning, to Robert Schapire and Yoav Freund for Adaboost. [sent-5, score-0.464]
5 There are several plausible reasons for this: There is no coherent community. [sent-7, score-0.151]
6 People drift in and out of the central conferences all the time. [sent-8, score-0.278]
7 Most of the author names from 10 years ago do not occur in the conferences of today. [sent-9, score-0.395]
8 There are at least a core group of people who have stayed around. [sent-11, score-0.171]
9 Machine Learning work doesn’t last Almost every paper is forgotten, because {the goals change, there isn’t any real progress, there are no teachable foundations}. [sent-12, score-0.089]
10 The field is fractured between many very different viewpoints—statistical, empirical, AI, and theoretical. [sent-15, score-0.17]
11 The prioritization of results across these very different viewpoints is hard. [sent-16, score-0.42]
12 Aspiration Perhaps the most valuable aspect of an award is that it gives people an incentive to aim for something in the long term. [sent-20, score-0.817]
13 The closest approximation that we have right now is “best papers” awards at individual conferences. [sent-21, score-0.406]
14 Best paper awards have a role, but it’s not the same. [sent-22, score-0.252]
15 10 years from now, when we look back 10 years, which papers will seem most significant? [sent-23, score-0.131]
16 Representation One function of an award is that tells other people what we consider good work. [sent-25, score-0.539]
17 In an academic reference frame, it gives information of the form “this person deserves tenure”. [sent-26, score-0.206]
18 An award has some role in furthering that process. [sent-29, score-0.6]
19 The worst part of any award is administering it. [sent-30, score-0.464]
20 How do you avoid wasting time and playing favorites while keeping the higher level vision of what might be useful in the long term? [sent-31, score-0.262]
wordName wordTfidf (topN-words)
[('award', 0.464), ('prize', 0.254), ('awards', 0.252), ('viewpoints', 0.154), ('closest', 0.154), ('turing', 0.14), ('role', 0.136), ('cs', 0.133), ('years', 0.131), ('perhaps', 0.12), ('conferences', 0.11), ('gives', 0.11), ('across', 0.103), ('crystallization', 0.096), ('fractured', 0.096), ('deserves', 0.096), ('forgotten', 0.096), ('drift', 0.096), ('aim', 0.096), ('favorites', 0.096), ('stayed', 0.096), ('wasting', 0.096), ('prioritization', 0.089), ('offering', 0.089), ('teachable', 0.089), ('clarity', 0.084), ('consensus', 0.084), ('occur', 0.08), ('cash', 0.08), ('frame', 0.08), ('prizes', 0.08), ('tenure', 0.077), ('insight', 0.077), ('placing', 0.077), ('yoav', 0.077), ('outsiders', 0.077), ('database', 0.077), ('reasons', 0.077), ('people', 0.075), ('biggest', 0.074), ('names', 0.074), ('coherent', 0.074), ('foundations', 0.074), ('different', 0.074), ('newer', 0.072), ('freund', 0.072), ('incentive', 0.072), ('central', 0.072), ('importantly', 0.07), ('playing', 0.07)]
simIndex simValue blogId blogTitle
same-blog 1 0.99999994 270 hunch net-2007-11-02-The Machine Learning Award goes to …
Introduction: Perhaps the biggest CS prize for research is the Turing Award , which has a $0.25M cash prize associated with it. It appears none of the prizes so far have been for anything like machine learning (the closest are perhaps database awards). In CS theory, there is the Gödel Prize which is smaller and newer, offering a $5K prize along and perhaps (more importantly) recognition. One such award has been given for Machine Learning, to Robert Schapire and Yoav Freund for Adaboost. In Machine Learning, there seems to be no equivalent of these sorts of prizes. There are several plausible reasons for this: There is no coherent community. People drift in and out of the central conferences all the time. Most of the author names from 10 years ago do not occur in the conferences of today. In addition, the entire subject area is fairly new. There are at least a core group of people who have stayed around. Machine Learning work doesn’t last Almost every paper is fo
2 0.18134223 457 hunch net-2012-02-29-Key Scientific Challenges and the Franklin Symposium
Introduction: For graduate students, the Yahoo! Key Scientific Challenges program including in machine learning is on again, due March 9 . The application is easy and the $5K award is high quality “no strings attached” funding. Consider submitting. Those in Washington DC, Philadelphia, and New York, may consider attending the Franklin Institute Symposium April 25 which has several speakers and an award for V . Attendance is free with an RSVP.
3 0.13425569 86 hunch net-2005-06-28-The cross validation problem: cash reward
Introduction: I just presented the cross validation problem at COLT . The problem now has a cash prize (up to $500) associated with it—see the presentation for details. The write-up for colt .
4 0.12321773 336 hunch net-2009-01-19-Netflix prize within epsilon
Introduction: The competitors for the Netflix Prize are tantalizingly close winning the million dollar prize. This year, BellKor and Commendo Research sent a combined solution that won the progress prize . Reading the writeups 2 is instructive. Several aspects of solutions are taken for granted including stochastic gradient descent, ensemble prediction, and targeting residuals (a form of boosting). Relatively to last year, it appears that many approaches have added parameterizations, especially for the purpose of modeling through time. The big question is: will they make the big prize? At this point, the level of complexity in entering the competition is prohibitive, so perhaps only the existing competitors will continue to try. (This equation might change drastically if the teams open source their existing solutions, including parameter settings.) One fear is that the progress is asymptoting on the wrong side of the 10% threshold. In the first year, the teams progressed through
5 0.11199456 371 hunch net-2009-09-21-Netflix finishes (and starts)
Introduction: I attended the Netflix prize ceremony this morning. The press conference part is covered fine elsewhere , with the basic outcome being that BellKor’s Pragmatic Chaos won over The Ensemble by 15-20 minutes , because they were tied in performance on the ultimate holdout set. I’m sure the individual participants will have many chances to speak about the solution. One of these is Bell at the NYAS ML symposium on Nov. 6 . Several additional details may interest ML people. The degree of overfitting exhibited by the difference in performance on the leaderboard test set and the ultimate hold out set was small, but determining at .02 to .03%. A tie was possible, because the rules cut off measurements below the fourth digit based on significance concerns. In actuality, of course, the scores do differ before rounding, but everyone I spoke to claimed not to know how. The complete dataset has been released on UCI , so each team could compute their own score to whatever accu
6 0.10541812 454 hunch net-2012-01-30-ICML Posters and Scope
7 0.09794347 93 hunch net-2005-07-13-“Sister Conference” presentations
8 0.095053434 428 hunch net-2011-03-27-Vowpal Wabbit, v5.1
9 0.094645493 22 hunch net-2005-02-18-What it means to do research.
10 0.094315149 430 hunch net-2011-04-11-The Heritage Health Prize
11 0.093174323 448 hunch net-2011-10-24-2011 ML symposium and the bears
12 0.091576211 134 hunch net-2005-12-01-The Webscience Future
13 0.091415688 452 hunch net-2012-01-04-Why ICML? and the summer conferences
14 0.089650229 59 hunch net-2005-04-22-New Blog: [Lowerbounds,Upperbounds]
15 0.089266725 435 hunch net-2011-05-16-Research Directions for Machine Learning and Algorithms
16 0.089021489 116 hunch net-2005-09-30-Research in conferences
17 0.088434674 233 hunch net-2007-02-16-The Forgetting
18 0.088278525 353 hunch net-2009-05-08-Computability in Artificial Intelligence
19 0.087732248 132 hunch net-2005-11-26-The Design of an Optimal Research Environment
20 0.086920686 309 hunch net-2008-07-10-Interesting papers, ICML 2008
topicId topicWeight
[(0, 0.211), (1, -0.062), (2, -0.031), (3, 0.074), (4, -0.032), (5, 0.014), (6, -0.036), (7, -0.006), (8, -0.01), (9, -0.053), (10, -0.011), (11, 0.114), (12, 0.001), (13, 0.017), (14, 0.029), (15, -0.001), (16, 0.065), (17, 0.056), (18, 0.004), (19, -0.059), (20, 0.004), (21, 0.061), (22, 0.041), (23, 0.012), (24, 0.062), (25, -0.048), (26, -0.062), (27, -0.024), (28, 0.012), (29, 0.015), (30, -0.069), (31, -0.038), (32, -0.009), (33, -0.022), (34, -0.037), (35, 0.046), (36, -0.059), (37, -0.025), (38, -0.014), (39, 0.112), (40, -0.192), (41, 0.009), (42, 0.1), (43, -0.028), (44, -0.057), (45, -0.108), (46, 0.003), (47, -0.002), (48, 0.136), (49, -0.053)]
simIndex simValue blogId blogTitle
same-blog 1 0.94181228 270 hunch net-2007-11-02-The Machine Learning Award goes to …
Introduction: Perhaps the biggest CS prize for research is the Turing Award , which has a $0.25M cash prize associated with it. It appears none of the prizes so far have been for anything like machine learning (the closest are perhaps database awards). In CS theory, there is the Gödel Prize which is smaller and newer, offering a $5K prize along and perhaps (more importantly) recognition. One such award has been given for Machine Learning, to Robert Schapire and Yoav Freund for Adaboost. In Machine Learning, there seems to be no equivalent of these sorts of prizes. There are several plausible reasons for this: There is no coherent community. People drift in and out of the central conferences all the time. Most of the author names from 10 years ago do not occur in the conferences of today. In addition, the entire subject area is fairly new. There are at least a core group of people who have stayed around. Machine Learning work doesn’t last Almost every paper is fo
2 0.62432653 336 hunch net-2009-01-19-Netflix prize within epsilon
Introduction: The competitors for the Netflix Prize are tantalizingly close winning the million dollar prize. This year, BellKor and Commendo Research sent a combined solution that won the progress prize . Reading the writeups 2 is instructive. Several aspects of solutions are taken for granted including stochastic gradient descent, ensemble prediction, and targeting residuals (a form of boosting). Relatively to last year, it appears that many approaches have added parameterizations, especially for the purpose of modeling through time. The big question is: will they make the big prize? At this point, the level of complexity in entering the competition is prohibitive, so perhaps only the existing competitors will continue to try. (This equation might change drastically if the teams open source their existing solutions, including parameter settings.) One fear is that the progress is asymptoting on the wrong side of the 10% threshold. In the first year, the teams progressed through
3 0.51508421 371 hunch net-2009-09-21-Netflix finishes (and starts)
Introduction: I attended the Netflix prize ceremony this morning. The press conference part is covered fine elsewhere , with the basic outcome being that BellKor’s Pragmatic Chaos won over The Ensemble by 15-20 minutes , because they were tied in performance on the ultimate holdout set. I’m sure the individual participants will have many chances to speak about the solution. One of these is Bell at the NYAS ML symposium on Nov. 6 . Several additional details may interest ML people. The degree of overfitting exhibited by the difference in performance on the leaderboard test set and the ultimate hold out set was small, but determining at .02 to .03%. A tie was possible, because the rules cut off measurements below the fourth digit based on significance concerns. In actuality, of course, the scores do differ before rounding, but everyone I spoke to claimed not to know how. The complete dataset has been released on UCI , so each team could compute their own score to whatever accu
4 0.4886224 339 hunch net-2009-01-27-Key Scientific Challenges
Introduction: Yahoo released the Key Scientific Challenges program. There is a Machine Learning list I worked on and a Statistics list which Deepak worked on. I’m hoping this is taken quite seriously by graduate students. The primary value, is that it gave us a chance to sit down and publicly specify directions of research which would be valuable to make progress on. A good strategy for a beginning graduate student is to pick one of these directions, pursue it, and make substantial advances for a PhD. The directions are sufficiently general that I’m sure any serious advance has applications well beyond Yahoo. A secondary point, (which I’m sure is primary for many ) is that there is money for graduate students here. It’s unrestricted, so you can use it for any reasonable travel, supplies, etc…
5 0.48578727 430 hunch net-2011-04-11-The Heritage Health Prize
Introduction: The Heritage Health Prize is potentially the largest prediction prize yet at $3M, which is sure to get many people interested. Several elements of the competition may be worth discussing. The most straightforward way for HPN to deploy this predictor is in determining who to cover with insurance. This might easily cover the costs of running the contest itself, but the value to the health system of a whole is minimal, as people not covered still exist. While HPN itself is a provider network, they have active relationships with a number of insurance companies, and the right to resell any entrant. It’s worth keeping in mind that the research and development may nevertheless end up being useful in the longer term, especially as entrants also keep the right to their code. The judging metric is something I haven’t seen previously. If a patient has probability 0.5 of being in the hospital 0 days and probability 0.5 of being in the hospital ~53.6 days, the optimal prediction in e
6 0.46754417 428 hunch net-2011-03-27-Vowpal Wabbit, v5.1
7 0.46424162 119 hunch net-2005-10-08-We have a winner
8 0.46273336 448 hunch net-2011-10-24-2011 ML symposium and the bears
9 0.46266416 212 hunch net-2006-10-04-Health of Conferences Wiki
10 0.46197149 248 hunch net-2007-06-19-How is Compressed Sensing going to change Machine Learning ?
11 0.46172521 172 hunch net-2006-04-14-JMLR is a success
12 0.46146423 1 hunch net-2005-01-19-Why I decided to run a weblog.
13 0.4595384 301 hunch net-2008-05-23-Three levels of addressing the Netflix Prize
14 0.45211893 93 hunch net-2005-07-13-“Sister Conference” presentations
15 0.44594613 98 hunch net-2005-07-27-Not goal metrics
16 0.44456595 414 hunch net-2010-10-17-Partha Niyogi has died
17 0.44320983 352 hunch net-2009-05-06-Machine Learning to AI
18 0.44311288 231 hunch net-2007-02-10-Best Practices for Collaboration
19 0.43986726 257 hunch net-2007-07-28-Asking questions
20 0.43926367 335 hunch net-2009-01-08-Predictive Analytics World
topicId topicWeight
[(27, 0.201), (38, 0.025), (53, 0.051), (55, 0.535), (68, 0.014), (77, 0.018), (94, 0.037), (95, 0.032)]
simIndex simValue blogId blogTitle
1 0.99653351 448 hunch net-2011-10-24-2011 ML symposium and the bears
Introduction: The New York ML symposium was last Friday. Attendance was 268, significantly larger than last year . My impression was that the event mostly still fit the space, although it was crowded. If anyone has suggestions for next year, speak up. The best student paper award went to Sergiu Goschin for a cool video of how his system learned to play video games (I can’t find the paper online yet). Choosing amongst the submitted talks was pretty difficult this year, as there were many similarly good ones. By coincidence all the invited talks were (at least potentially) about faster learning algorithms. Stephen Boyd talked about ADMM . Leon Bottou spoke on single pass online learning via averaged SGD . Yoav Freund talked about parameter-free hedging . In Yoav’s case the talk was mostly about a better theoretical learning algorithm, but it has the potential to unlock an exponential computational complexity improvement via oraclization of experts algorithms… but some serious
2 0.99518979 20 hunch net-2005-02-15-ESPgame and image labeling
Introduction: Luis von Ahn has been running the espgame for awhile now. The espgame provides a picture to two randomly paired people across the web, and asks them to agree on a label. It hasn’t managed to label the web yet, but it has produced a large dataset of (image, label) pairs. I organized the dataset so you could explore the implied bipartite graph (requires much bandwidth). Relative to other image datasets, this one is quite large—67000 images, 358,000 labels (average of 5/image with variation from 1 to 19), and 22,000 unique labels (one every 3 images). The dataset is also very ‘natural’, consisting of images spidered from the internet. The multiple label characteristic is intriguing because ‘learning to learn’ and metalearning techniques may be applicable. The ‘natural’ quality means that this dataset varies greatly in difficulty from easy (predicting “red”) to hard (predicting “funny”) and potentially more rewarding to tackle. The open problem here is, of course, to make
3 0.994609 90 hunch net-2005-07-07-The Limits of Learning Theory
Introduction: Suppose we had an infinitely powerful mathematician sitting in a room and proving theorems about learning. Could he solve machine learning? The answer is “no”. This answer is both obvious and sometimes underappreciated. There are several ways to conclude that some bias is necessary in order to succesfully learn. For example, suppose we are trying to solve classification. At prediction time, we observe some features X and want to make a prediction of either 0 or 1 . Bias is what makes us prefer one answer over the other based on past experience. In order to learn we must: Have a bias. Always predicting 0 is as likely as 1 is useless. Have the “right” bias. Predicting 1 when the answer is 0 is also not helpful. The implication of “have a bias” is that we can not design effective learning algorithms with “a uniform prior over all possibilities”. The implication of “have the ‘right’ bias” is that our mathematician fails since “right” is defined wi
4 0.99338698 302 hunch net-2008-05-25-Inappropriate Mathematics for Machine Learning
Introduction: Reviewers and students are sometimes greatly concerned by the distinction between: An open set and a closed set . A Supremum and a Maximum . An event which happens with probability 1 and an event that always happens. I don’t appreciate this distinction in machine learning & learning theory. All machine learning takes place (by definition) on a machine where every parameter has finite precision. Consequently, every set is closed, a maximal element always exists, and probability 1 events always happen. The fundamental issue here is that substantial parts of mathematics don’t appear well-matched to computation in the physical world, because the mathematics has concerns which are unphysical. This mismatched mathematics makes irrelevant distinctions. We can ask “what mathematics is appropriate to computation?” Andrej has convinced me that a pretty good answer to this question is constructive mathematics . So, here’s a basic challenge: Can anyone name a situati
5 0.99043882 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge
Introduction: The results have been posted , with CMU first , Stanford second , and Virginia Tech Third . Considering that this was an open event (at least for people in the US), this was a very strong showing for research at universities (instead of defense contractors, for example). Some details should become public at the NIPS workshops . Slashdot has a post with many comments.
6 0.98950362 446 hunch net-2011-10-03-Monday announcements
same-blog 7 0.97332698 270 hunch net-2007-11-02-The Machine Learning Award goes to …
8 0.97137076 472 hunch net-2012-08-27-NYAS ML 2012 and ICML 2013
9 0.96270323 331 hunch net-2008-12-12-Summer Conferences
10 0.96130502 395 hunch net-2010-04-26-Compassionate Reviewing
11 0.94450372 453 hunch net-2012-01-28-Why COLT?
12 0.92744708 326 hunch net-2008-11-11-COLT CFP
13 0.92744708 465 hunch net-2012-05-12-ICML accepted papers and early registration
14 0.91482103 387 hunch net-2010-01-19-Deadline Season, 2010
15 0.91480857 65 hunch net-2005-05-02-Reviewing techniques for conferences
16 0.89219302 452 hunch net-2012-01-04-Why ICML? and the summer conferences
17 0.86712134 356 hunch net-2009-05-24-2009 ICML discussion site
18 0.85149622 40 hunch net-2005-03-13-Avoiding Bad Reviewing
19 0.85065132 443 hunch net-2011-09-03-Fall Machine Learning Events
20 0.84903926 457 hunch net-2012-02-29-Key Scientific Challenges and the Franklin Symposium