hunch_net hunch_net-2009 hunch_net-2009-335 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Carla Vicens and Eric Siegel contacted me about Predictive Analytics World in San Francisco February 18&19, which I wasn’t familiar with. A quick look at the agenda reveals several people I know working on applications of machine learning in businesses, covering deployed applications topics. It’s interesting to see a business-focused machine learning conference, as it says that we are succeeding as a field. If you are interested in deployed applications, you might attend. Eric and I did a quick interview by email. John > I’ve mostly published and participated in academic machine learning conferences like ICML, COLT, and NIPS. When I look at the set of speakers and subjects for your conference I think “machine learning for business”. Is that your understanding of things? What I’m trying to ask is: what do you view as the primary goal for this conference? Eric > You got it. This is the business event focused on the commercial deployment of technology developed at
sentIndex sentText sentNum sentScore
1 A quick look at the agenda reveals several people I know working on applications of machine learning in businesses, covering deployed applications topics. [sent-2, score-0.496]
2 It’s interesting to see a business-focused machine learning conference, as it says that we are succeeding as a field. [sent-3, score-0.061]
3 If you are interested in deployed applications, you might attend. [sent-4, score-0.105]
4 John > I’ve mostly published and participated in academic machine learning conferences like ICML, COLT, and NIPS. [sent-6, score-0.292]
5 When I look at the set of speakers and subjects for your conference I think “machine learning for business”. [sent-7, score-0.088]
6 This is the business event focused on the commercial deployment of technology developed at the research conferences you named. [sent-11, score-1.448]
7 Academics’ term, “machine learning,” is essentially synonymous with the business world’s “predictive modeling”. [sent-12, score-0.464]
8 Predictive Analytics World focuses on business applications of this technology, such as response modeling, churn modeling, email targeting, product recommendations, insurance pricing, and credit scoring. [sent-13, score-0.514]
9 PAW’s goal is to strengthen the business impact delivered by predictive analytics deployment, and establish new opportunities with predictive analytics. [sent-14, score-1.293]
10 The conference delivers case studies, expertise and resources to this end. [sent-15, score-0.088]
11 John > People at academic conferences would hope that technology developed there can transfer into business use. [sent-17, score-0.89]
12 Eric > The best way to catalyze commercial deployment is to show the people it really works outside “the lab” – which is why PAW’s program is packed primarily with named case studies of commercial deployment. [sent-20, score-1.066]
13 These success stories answer your question with a resounding “yes” that the core technology developed academically is indeed put to use. [sent-21, score-0.536]
14 But predictive analytics has not yet been broadly adopted across all industries, although success stories show at least initial reach in each vertical. [sent-22, score-0.85]
15 So, sure, as one who previously wore a researcher’s hat, commercial deployment can feel slow; having solved the hardest theoretical, mathematical or statistical problems, the rest should go smoothly, right? [sent-23, score-0.666]
16 The main challenges come in ramping up the business “consumer” on the technology so they see its value, positioning the technology in a way that provides business value, and, on the integration side, in preparing corporate data for predictive modeling (that’s a doozy! [sent-25, score-1.948]
17 John > Sometimes people working in the academic world don’t have a good understanding of what the real problems are. [sent-28, score-0.216]
18 Do you have a sense of which areas of research are underemphasized in the academic world? [sent-29, score-0.117]
19 Eric > To reach commercial success in deploying predictive analytics for the business applications I listed above, the main challenges are on the process and non-analytical integration side, rather than core machine learning technology; its good enough. [sent-30, score-1.923]
20 So, I don’t consider there to be glaring ommissions in the capabilities of core machine learning (I taught the machine learning graduate course at Columbia University and still consider Tom Mitchell’s textbook to be my bible). [sent-31, score-0.197]
wordName wordTfidf (topN-words)
[('business', 0.411), ('commercial', 0.316), ('deployment', 0.297), ('analytics', 0.293), ('predictive', 0.268), ('eric', 0.231), ('technology', 0.206), ('modeling', 0.146), ('paw', 0.119), ('academic', 0.117), ('challenges', 0.109), ('deployed', 0.105), ('applications', 0.103), ('world', 0.099), ('developed', 0.095), ('conference', 0.088), ('stories', 0.084), ('john', 0.083), ('studies', 0.081), ('success', 0.076), ('core', 0.075), ('integration', 0.075), ('reach', 0.073), ('quick', 0.071), ('prize', 0.07), ('speak', 0.066), ('main', 0.063), ('focused', 0.062), ('machine', 0.061), ('conferences', 0.061), ('side', 0.056), ('show', 0.056), ('participated', 0.053), ('consumer', 0.053), ('ramping', 0.053), ('interview', 0.053), ('marketing', 0.053), ('agenda', 0.053), ('columbia', 0.053), ('establish', 0.053), ('industries', 0.053), ('hardest', 0.053), ('contacted', 0.053), ('pricing', 0.053), ('synonymous', 0.053), ('wishes', 0.053), ('language', 0.051), ('hat', 0.049), ('leader', 0.049), ('businesses', 0.049)]
simIndex simValue blogId blogTitle
same-blog 1 1.0000004 335 hunch net-2009-01-08-Predictive Analytics World
Introduction: Carla Vicens and Eric Siegel contacted me about Predictive Analytics World in San Francisco February 18&19, which I wasn’t familiar with. A quick look at the agenda reveals several people I know working on applications of machine learning in businesses, covering deployed applications topics. It’s interesting to see a business-focused machine learning conference, as it says that we are succeeding as a field. If you are interested in deployed applications, you might attend. Eric and I did a quick interview by email. John > I’ve mostly published and participated in academic machine learning conferences like ICML, COLT, and NIPS. When I look at the set of speakers and subjects for your conference I think “machine learning for business”. Is that your understanding of things? What I’m trying to ask is: what do you view as the primary goal for this conference? Eric > You got it. This is the business event focused on the commercial deployment of technology developed at
2 0.12707922 369 hunch net-2009-08-27-New York Area Machine Learning Events
Introduction: Several events are happening in the NY area. Barriers in Computational Learning Theory Workshop, Aug 28. That’s tomorrow near Princeton. I’m looking forward to speaking at this one on “Getting around Barriers in Learning Theory”, but several other talks are of interest, particularly to the CS theory inclined. Claudia Perlich is running the INFORMS Data Mining Contest with a deadline of Sept. 25. This is a contest using real health record data (they partnered with HealthCare Intelligence ) to predict transfers and mortality. In the current US health care reform debate, the case studies of high costs we hear strongly suggest machine learning & statistics can save many billions. The Singularity Summit October 3&4 . This is for the AIists out there. Several of the talks look interesting, although unfortunately I’ll miss it for ALT . Predictive Analytics World, Oct 20-21 . This is stretching the definition of “New York Area” a bit, but the train to DC is reasonable.
3 0.10929622 475 hunch net-2012-10-26-ML Symposium and Strata-Hadoop World
Introduction: The New York ML symposium was last Friday. There were 303 registrations, up a bit from last year . I particularly enjoyed talks by Bill Freeman on vision and ML, Jon Lenchner on strategy in Jeopardy, and Tara N. Sainath and Brian Kingsbury on deep learning for speech recognition . If anyone has suggestions or thoughts for next year, please speak up. I also attended Strata + Hadoop World for the first time. This is primarily a trade conference rather than an academic conference, but I found it pretty interesting as a first time attendee. This is ground zero for the Big data buzzword, and I see now why. It’s about data, and the word “big” is so ambiguous that everyone can lay claim to it. There were essentially zero academic talks. Instead, the focus was on war stories, product announcements, and education. The general level of education is much lower—explaining Machine Learning to the SQL educated is the primary operating point. Nevertheless that’s happening, a
4 0.094152518 158 hunch net-2006-02-24-A Fundamentalist Organization of Machine Learning
Introduction: There are several different flavors of Machine Learning classes. Many classes are of the ‘zoo’ sort: many different learning algorithms are presented. Others avoid the zoo by not covering the full scope of machine learning. This is my view of what makes a good machine learning class, along with why. I’d like to specifically invite comment on whether things are missing, misemphasized, or misplaced. Phase Subject Why? Introduction What is a machine learning problem? A good understanding of the characteristics of machine learning problems seems essential. Characteristics include: a data source, some hope the data is predictive, and a need for generalization. This is probably best taught in a case study manner: lay out the specifics of some problem and then ask “Is this a machine learning problem?” Introduction Machine Learning Problem Identification Identification and recognition of the type of learning problems is (obviously) a very important step i
5 0.090447612 342 hunch net-2009-02-16-KDNuggets
Introduction: Eric Zaetsch points out KDNuggets which is a well-developed mailing list/news site with a KDD flavor. This might particularly interest people looking for industrial jobs in machine learning, as the mailing list has many such.
6 0.088345043 452 hunch net-2012-01-04-Why ICML? and the summer conferences
7 0.086401299 110 hunch net-2005-09-10-“Failure” is an option
8 0.084335014 250 hunch net-2007-06-23-Machine Learning Jobs are Growing on Trees
9 0.080214255 314 hunch net-2008-08-24-Mass Customized Medicine in the Future?
10 0.080063544 193 hunch net-2006-07-09-The Stock Prediction Machine Learning Problem
11 0.080018304 282 hunch net-2008-01-06-Research Political Issues
12 0.079927087 112 hunch net-2005-09-14-The Predictionist Viewpoint
13 0.079767883 21 hunch net-2005-02-17-Learning Research Programs
14 0.077566929 42 hunch net-2005-03-17-Going all the Way, Sometimes
15 0.075514205 378 hunch net-2009-11-15-The Other Online Learning
16 0.073858909 235 hunch net-2007-03-03-All Models of Learning have Flaws
17 0.073331989 141 hunch net-2005-12-17-Workshops as Franchise Conferences
18 0.071021639 454 hunch net-2012-01-30-ICML Posters and Scope
19 0.070887305 135 hunch net-2005-12-04-Watchword: model
20 0.068916157 425 hunch net-2011-02-25-Yahoo! Machine Learning grant due March 11
topicId topicWeight
[(0, 0.162), (1, -0.057), (2, -0.087), (3, 0.018), (4, -0.047), (5, -0.039), (6, -0.024), (7, 0.041), (8, -0.013), (9, -0.055), (10, 0.012), (11, 0.034), (12, 0.007), (13, 0.014), (14, 0.041), (15, 0.022), (16, -0.002), (17, 0.02), (18, 0.022), (19, -0.054), (20, -0.005), (21, -0.05), (22, 0.014), (23, -0.001), (24, -0.037), (25, -0.06), (26, -0.015), (27, 0.006), (28, -0.002), (29, 0.024), (30, -0.063), (31, 0.082), (32, -0.015), (33, -0.018), (34, 0.065), (35, 0.005), (36, -0.033), (37, -0.004), (38, 0.052), (39, -0.036), (40, -0.057), (41, 0.019), (42, 0.076), (43, -0.032), (44, -0.099), (45, 0.035), (46, -0.009), (47, -0.103), (48, -0.056), (49, 0.029)]
simIndex simValue blogId blogTitle
same-blog 1 0.94567978 335 hunch net-2009-01-08-Predictive Analytics World
Introduction: Carla Vicens and Eric Siegel contacted me about Predictive Analytics World in San Francisco February 18&19, which I wasn’t familiar with. A quick look at the agenda reveals several people I know working on applications of machine learning in businesses, covering deployed applications topics. It’s interesting to see a business-focused machine learning conference, as it says that we are succeeding as a field. If you are interested in deployed applications, you might attend. Eric and I did a quick interview by email. John > I’ve mostly published and participated in academic machine learning conferences like ICML, COLT, and NIPS. When I look at the set of speakers and subjects for your conference I think “machine learning for business”. Is that your understanding of things? What I’m trying to ask is: what do you view as the primary goal for this conference? Eric > You got it. This is the business event focused on the commercial deployment of technology developed at
2 0.61327326 250 hunch net-2007-06-23-Machine Learning Jobs are Growing on Trees
Introduction: The consensus of several discussions at ICML is that the number of jobs for people knowing machine learning well substantially exceeds supply. This is my experience as well. Demand comes from many places, but I’ve seen particularly strong demand from trading companies and internet startups. Like all interest bursts, this one will probably pass because of economic recession or other distractions. Nevertheless, the general outlook for machine learning in business seems to be good. Machine learning is all about optimization when there is uncertainty and lots of data. The quantity of data available is growing quickly as computer-run processes and sensors become more common, and the quality of the data is dropping since there is little editorial control in it’s collection. Machine Learning is a difficult subject to master (*), so those who do should remain in demand over the long term. (*) In fact, it would be reasonable to claim that no one has mastered it—there are just some peo
3 0.57755876 21 hunch net-2005-02-17-Learning Research Programs
Introduction: This is an attempt to organize the broad research programs related to machine learning currently underway. This isn’t easy—this map is partial, the categories often overlap, and there are many details left out. Nevertheless, it is (perhaps) helpful to have some map of what is happening where. The word ‘typical’ should not be construed narrowly here. Learning Theory Focuses on analyzing mathematical models of learning, essentially no experiments. Typical conference: COLT. Bayesian Learning Bayes law is always used. Focus on methods of speeding up or approximating integration, new probabilistic models, and practical applications. Typical conferences: NIPS,UAI Structured learning Predicting complex structured outputs, some applications. Typiical conferences: NIPS, UAI, others Reinforcement Learning Focused on ‘agent-in-the-world’ learning problems where the goal is optimizing reward. Typical conferences: ICML Unsupervised Learning/Clustering/Dimensionality Reduc
4 0.56674343 290 hunch net-2008-02-27-The Stats Handicap
Introduction: Graduating students in Statistics appear to be at a substantial handicap compared to graduating students in Machine Learning, despite being in substantially overlapping subjects. The problem seems to be cultural. Statistics comes from a mathematics background which emphasizes large publications slowly published under review at journals. Machine Learning comes from a Computer Science background which emphasizes quick publishing at reviewed conferences. This has a number of implications: Graduating statistics PhDs often have 0-2 publications while graduating machine learning PhDs might have 5-15. Graduating ML students have had a chance for others to build on their work. Stats students have had no such chance. Graduating ML students have attended a number of conferences and presented their work, giving them a chance to meet people. Stats students have had fewer chances of this sort. In short, Stats students have had relatively few chances to distinguish themselves and
5 0.56593305 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…
6 0.56410474 369 hunch net-2009-08-27-New York Area Machine Learning Events
7 0.55534142 389 hunch net-2010-02-26-Yahoo! ML events
8 0.55447596 48 hunch net-2005-03-29-Academic Mechanism Design
9 0.55434817 475 hunch net-2012-10-26-ML Symposium and Strata-Hadoop World
10 0.55394638 228 hunch net-2007-01-15-The Machine Learning Department
11 0.51871789 106 hunch net-2005-09-04-Science in the Government
12 0.51300341 406 hunch net-2010-08-22-KDD 2010
13 0.50671571 49 hunch net-2005-03-30-What can Type Theory teach us about Machine Learning?
14 0.50660533 270 hunch net-2007-11-02-The Machine Learning Award goes to …
15 0.50652128 342 hunch net-2009-02-16-KDNuggets
16 0.50514156 232 hunch net-2007-02-11-24
17 0.50489312 110 hunch net-2005-09-10-“Failure” is an option
18 0.50466686 171 hunch net-2006-04-09-Progress in Machine Translation
19 0.5024938 452 hunch net-2012-01-04-Why ICML? and the summer conferences
20 0.50178474 195 hunch net-2006-07-12-Who is having visa problems reaching US conferences?
topicId topicWeight
[(4, 0.013), (10, 0.01), (27, 0.149), (38, 0.048), (46, 0.012), (53, 0.048), (54, 0.368), (55, 0.06), (64, 0.019), (72, 0.015), (94, 0.073), (95, 0.066)]
simIndex simValue blogId blogTitle
1 0.96317458 376 hunch net-2009-11-06-Yisong Yue on Self-improving Systems
Introduction: I’d like to point out Yisong Yue ‘s post on Self-improving systems , which is a nicely readable description of the necessity and potential of interactive learning to deal with the information overload problem that is endemic to the modern internet.
2 0.91143245 494 hunch net-2014-03-11-The New York ML Symposium, take 2
Introduction: The 20 13 14 is New York Machine Learning Symposium is finally happening on March 28th at the New York Academy of Science . Every invited speaker interests me personally. They are: Rayid Ghani (Chief Scientist at Obama 2012) Brian Kingsbury (Speech Recognition @ IBM) Jorge Nocedal (who did LBFGS) We’ve been somewhat disorganized in advertising this. As a consequence, anyone who has not submitted an abstract but would like to do so may send one directly to me (jl@hunch.net title NYASMLS) by Friday March 14. I will forward them to the rest of the committee for consideration.
same-blog 3 0.85608989 335 hunch net-2009-01-08-Predictive Analytics World
Introduction: Carla Vicens and Eric Siegel contacted me about Predictive Analytics World in San Francisco February 18&19, which I wasn’t familiar with. A quick look at the agenda reveals several people I know working on applications of machine learning in businesses, covering deployed applications topics. It’s interesting to see a business-focused machine learning conference, as it says that we are succeeding as a field. If you are interested in deployed applications, you might attend. Eric and I did a quick interview by email. John > I’ve mostly published and participated in academic machine learning conferences like ICML, COLT, and NIPS. When I look at the set of speakers and subjects for your conference I think “machine learning for business”. Is that your understanding of things? What I’m trying to ask is: what do you view as the primary goal for this conference? Eric > You got it. This is the business event focused on the commercial deployment of technology developed at
4 0.83602643 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 0.67312288 458 hunch net-2012-03-06-COLT-ICML Open Questions and ICML Instructions
Introduction: Sasha is the open problems chair for both COLT and ICML . Open problems will be presented in a joint session in the evening of the COLT/ICML overlap day. COLT has a history of open sessions, but this is new for ICML. If you have a difficult theoretically definable problem in machine learning, consider submitting it for review, due March 16 . You’ll benefit three ways: The effort of writing down a precise formulation of what you want often helps you understand the nature of the problem. Your problem will be officially published and citable. You might have it solved by some very intelligent bored people. The general idea could easily be applied to any problem which can be crisply stated with an easily verifiable solution, and we may consider expanding this in later years, but for this year all problems need to be of a theoretical variety. Joelle and I (and Mahdi , and Laurent ) finished an initial assignment of Program Committee and Area Chairs to pap
6 0.66310096 33 hunch net-2005-02-28-Regularization
7 0.50555122 10 hunch net-2005-02-02-Kolmogorov Complexity and Googling
8 0.46175298 360 hunch net-2009-06-15-In Active Learning, the question changes
9 0.4613646 132 hunch net-2005-11-26-The Design of an Optimal Research Environment
10 0.45929101 343 hunch net-2009-02-18-Decision by Vetocracy
11 0.45865554 95 hunch net-2005-07-14-What Learning Theory might do
12 0.45764962 286 hunch net-2008-01-25-Turing’s Club for Machine Learning
13 0.45757845 475 hunch net-2012-10-26-ML Symposium and Strata-Hadoop World
14 0.45681995 371 hunch net-2009-09-21-Netflix finishes (and starts)
15 0.45602819 36 hunch net-2005-03-05-Funding Research
16 0.45595515 235 hunch net-2007-03-03-All Models of Learning have Flaws
17 0.45420182 478 hunch net-2013-01-07-NYU Large Scale Machine Learning Class
18 0.45410165 359 hunch net-2009-06-03-Functionally defined Nonlinear Dynamic Models
19 0.45405301 370 hunch net-2009-09-18-Necessary and Sufficient Research
20 0.45323434 351 hunch net-2009-05-02-Wielding a New Abstraction