0900 Registration, poster set-up, and continental breakfast 0930 Welcome 0945 Invited Talk.pdf

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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