JiscMail Logo
Email discussion lists for the UK Education and Research communities

Help for SUPPORT-VECTOR-MACHINES Archives


SUPPORT-VECTOR-MACHINES Archives

SUPPORT-VECTOR-MACHINES Archives


SUPPORT-VECTOR-MACHINES@JISCMAIL.AC.UK


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

SUPPORT-VECTOR-MACHINES Home

SUPPORT-VECTOR-MACHINES Home

SUPPORT-VECTOR-MACHINES  October 2010

SUPPORT-VECTOR-MACHINES October 2010

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

NIPS 2010 Workshop on "Machine Learning in Online ADvertising" (MLOAD): Final CFP

From:

"Dr. James G. Shanahan" <[log in to unmask]>

Reply-To:

The Support Vector Machine discussion list <[log in to unmask]>

Date:

Thu, 14 Oct 2010 12:26:56 -0700

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (131 lines)

##########################################################

Final CALL FOR PAPERS

     NIPS 2010 Workshop on
     Machine Learning in Online ADvertising (MLOAD)
     December 10, 2010
     Whistler, B.C. Canada

    http://research.microsoft.com/~mload-2010

IMPORTANT DATES

Submission deadline:          Oct. 23, 2010
Notification of Acceptance:  Nov. 11, 2010
Camera ready:                   Nov. 22, 2010
Workshop Date:                 Dec. 10, 2010

OVERVIEW

Online advertising, a form of advertising that utilizes the Internet
and World Wide Web to deliver marketing messages and attract
customers, has seen exponential growth since its inception over
15 years ago, resulting in a $65 billion market worldwide in 2008;
it has been pivotal to the success of the World Wide Web. This
success has arisen largely from the transformation of the
advertising industry from a low-tech, human intensive, “Mad Men”
(ref. HBO TV Series) way of doing work (that were common
place for much of the 20th century and the early days of online
advertising) to highly optimized, mathematical, machine
learning-centric processes (some of which have been adapted
from Wall Street) that form the backbone of many current online
advertising systems.

The dramatic growth of online advertising poses great challenges
to the machine learning research community and calls for new
technologies to be developed. Online advertising is a complex
problem, especially from machine learning point of view. It
contains multiple parties (i.e., advertisers, users, publishers,
and ad platforms such as ad exchanges), which interact with
each other harmoniously but exhibit a conflict of interest when it
comes to risk and revenue objectives.  It is highly dynamic in
terms of the rapid change of user information needs, non-stationary
bids of advertisers, and the frequent modifications of ads
campaigns. It is very large scale, with billions of keywords, tens of
millions of ads, billions of users,  millions of advertisers where
events such as clicks and actions can be extremely rare. In
addition, the field lies at intersection of machine learning,
economics, optimization, distributed systems and information
science all very advanced and complex fields in their own right.
For such a complex problem, conventional machine learning
technologies and evaluation methodologies are not be sufficient,
and the development of new algorithms and theories is sorely needed.

The goal of this workshop is to overview the state of the art in
online advertising, and to discuss future directions and challenges
in research and development, from a machine learning point of
view. We expect the workshop to help develop a community of
researchers who are interested in this area, and yield future
collaboration and exchanges.

Possible topics include:

1) Dynamic/non-stationary/online learning algorithms for online advertising
2) Large scale machine learning for online advertising
3) Learning theory for online advertising
4) Learning to rank for ads display
5) Auction mechanism design for paid search
6) Social network advertising and micro-blog advertising
7) System modeling for ad platform
8) Traffic and click through rate prediction
9) Bids optimization
10) Metrics and evaluation
11) Yield optimization
12) Behavioral targeting modeling
13) Click fraud detection
14) Privacy in advertising
15) Crowd sourcing and inference
16) Mobile advertising and social advertising
17) Public datasets creation for research on online advertising

The above list is not exhaustive, and we welcome submissions on highly
related topics too.


KEYNOTE SPEAKERS

  -- Foster Provost (New York University)
  -- Art Owen (Stanford University)


INVITED SPEAKERS (tentative)

  -- Ashish Goel (Stanford University)
  -- Thore Graepel, Microsoft Research
  -- Jianchang Mao (Yahoo! Labs)


WORKSHOP FORMAT

Broadly, this one-day workshop aims at exploring the current
challenges in developing and applying machine learning to online
advertising. It will explore these topics in tutorials and invited talks.
In addition, we will have a poster session with spotlight presentations
to provide a platform for presenting new contributions.


SUBMISSION DETAILS

Submissions to the MLOAD workshop should be in the format
of extended abstracts; 4-6 pages formatted in the NIPS style.
The submission does not need to be blind.  Please upload
submissions in PDF  to https://cmt.research.microsoft.com/MLOAD2010/.
Accepted extended abstracts will be made available online
at the workshop website. In addition, we plan to invite extended
versions of selected papers for a special issue of a top-tier
machine learning or information retrieval journal (under discussion).


ORGANIZING COMMITTEE

 -- Deepak K. Agarwal (Yahoo! Research)
 -- Tie-Yan Liu (Microsoft Research Asia)
 -- Tao Qin (Microsoft Research Asia)
 -- James G. Shanahan (Independent Consultant)


MLOAD CONTACT

Jimi Shanahan: James_DOT_Shanahan_AT_gmail_DOT_com

Top of Message | Previous Page | Permalink

JiscMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
2006
2005
2004
2003
2002
2001
2000


JiscMail is a Jisc service.

View our service policies at https://www.jiscmail.ac.uk/policyandsecurity/ and Jisc's privacy policy at https://www.jisc.ac.uk/website/privacy-notice

Secured by F-Secure Anti-Virus CataList Email List Search Powered by the LISTSERV Email List Manager