0900 Registration, poster set-up, and continental breakfast 0930 Welcome 0945 Invited Talk.pdf
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0900 Registration, poster set-up, and continental breakfast 0930 Welcome 0945 Invited Talk
Machine Learning:
Theory, Applications, Experiences
W
iM
L 2
00
7
October 17, 2007
Royal Plaza Hotel
Orlando, Florida
ELEVATOR
GUEST LAUNDRYL
Schedule
09:00
Registration, poster set-up, and continental breakfast
09:30
Welcome
09:45
Invited Talk: Machine Learning in Space
Kiri L. Wagstaff, N.A.S.A.
10:15
A general agnostic active learning algorithm
Claire Monteleoni, UC San Diego
10:35
Bayesian Nonparametric Regression with Local Models
Jo-Anne Ting, University of Southern California
10:55
Coffee Break
11:15
Invited Talk: Applying machine learning to a real-world
problem: real-time ranking of electric components
Marta Arias, Columbia University
11:45
Generating Summary Keywords for Emails Using Topics.
Hanna Wallach, University of Cambridge
12:05
Continuous-State POMDPs with Hybrid Dynamics
Emma Brunskill, MIT
12:25
Spotlights
12:45
Lunch
14:20
Invited Talk: Randomized Approaches to Preserving Privacy
Nina Mishra, University of Virginia
14:50
Clustering Social Networks
Isabelle Stanton, University of Virginia
15:10
Coffee Break
15:30
Invited Talk: Applications of Machine Learning to Image
Retrieval
Sally Goldman, Washington University
16:00
Improvement in Performance of Learning Using Scaling
Soumi Ray, University of Maryland Baltimore County
16:20
Poster Session
17:10
Panel/ Open Discussion
17:40
Concluding Remarks
Invited Talks
Machine Learning in Space
Kiri L. Wagstaff, N.A.S.A.
Remote space environments simultaneously present significant challenges to the
machine learning community and enormous opportunities for advancement. In
this talk, I present recent work on three key issues associated with machine
learning in space: on-board data classification and regression, on-board
prioritization of analysis results, and reliable computing in high-radiation
environments. Support vector machines are currently be
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