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The SAMSI-FODAVA Workshop on Interactive Visualization and Analysis of Massive Data will be held on December 10-12, 2012.
Posted: October 02, 2012
The FODAVA Annual Meeting will immediately follow (Dec 12-13) the SAMSI/FODAVA joint workshop at the same location.
Posted: September 05, 2012
Many of the modern data sets such as text and image data can be represented in high-dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from num
Posted: June 30, 2012

Statistical Machine Learning for Visual Analytics

NIPS*2009 Workshop
December 11, 2009
Glacier room, Westin Hotel, Whistler BC


Guy Lebanon
Georgia Institute of Technology
lebanon (a)
Fei Sha
University of Southern California
feisha (a)


Background and Motivation

As the amount and complexity of available information grows, it becomes clear that traditional data analysis methods are insufficient. In particular, the data analysis process becomes inherently iterative and interactive: i) users start analysis with a vague modeling assumption (expressed often as a form of domain knowledge) about the data; ii) data are analyzed and the intermediate results are visually presented to the users; iii) users revise modeling assumptions and the process iterates. This process has emerged as a prominent framework in many data analysis application areas including business, homeland security, and health care. This framework, known succinctly as visual analytics, combines visualization, human computer interaction, and statistical data analysis in order to derive insight from massive high dimensional data.

Many statistical learning techniques, for instance, dimensionality reduction for information visualization and navigation, are fundamental tools in visual analytics. Addressing new challenges -- being iterative and interactive -- has potential to go beyond the limits of traditional techniques. However, to realize its potential, there is a need to develop new theory and methodology that bridges visualization, interaction, and statistical learning.

The purpose of this workshop is to expose the NIPS audience to this new and exciting interdisciplinary area and to foster the creation of a new specialization within the machine learning community: machine learning for visual analytics.


Call for Papers

We invite submission of short papers, to be presented as posters during the poster session and as spotlights (schedule permitting). Papers up to three pages in nips format should be submitted to one of the organizers (see above for email). Submission deadline is November 20th.

Preliminary Schedule

7:30-8:00 New Directions in Text Visualization Guy Lebanon
Georgia Institute of Technology
8:00-8:30 [need title] Ping Li
Cornell University
8:300-9:00 [need title] Amir Globerson
The Hebrew University
9:00-9:15 Coffee Break  
9:15-9:55 Three New Ideas in Manifold Learning
based on Semidefinite Programming, for High-Dimensional Visualization
Alex Gray
Georgia Institute of Technology
10:00-10:30 Future Challenges and
Open Problems in Machine Learning for Visual Analytics

Panel Discussion
3:30-4:00   Fei Sha
University of Southern Calirfonia
4:00-4:30 Visual Analytics for Networks [need title] Jure Leskovec
Stanford University
4:30-5:00 Dimensionality Reduction [need title] Chris Burges
Microsoft Research
5:00-5:30 [need title] Sayan Mukherjee
Duke University
5:30-5:45 Coffee Break  
5:45-6:15 Visual Analytics for Audio Mark Hasegawa-Johnson
University of Illinois, Urbana-Champaign
6:15-6:45 Poster Session