1. Presenter: Dale Schuurmans

2. 2 Helping the world understand data and make informed decisions Core technology research: Underlies prediction and control, Underlies most applications, Involves almost every PI

3. 3 Almost all data is now digital Text, images, audio, video (media) Scientific, financial, commercial, industrial data Robotics (sensors, telemetry), surveillance Data is complex Video, text, microarrays, … Data is accumulating Data analysis is a major global industry Web search, finance Social and economic impact But task embodies hard scientific problems

4. 4 Discovering intrinsic structure in data Classes Manifolds Components Constraints Dynamics (Usually unsupervised)

5. 5 Unifying challenge for AICML Underlies many other thrusts, applications Fundamental research New principles New algorithms New ideas AICML advantage Critical mass Outstanding resources Diversity of applications

6. 6 1. Large margin clustering (completed; led to Project 2) 2. Convex, discriminative EM (ongoing; poster #16) 3. Bicluster coding (ongoing; poster #8) 4. Manifold dimension estimation (ongoing; poster #13) 5. Action respecting embedding (ongoing; poster #14)

7. 7 6. Temporal difference networks (ongoing; poster #19) 7. Opponent modeling (ongoing; posters #9, #11) 8. WWW visualization (ongoing; posters #3, #4) And other projects …

8. 8 Primary PI ’ s Goebel, Greiner, Holte, Bowling, Schuurmans, Sutton, Szepesvari 5 PDFs 17 Grad students 4 Technical staff

9. 9 Grants $225K CFI New Opportunities Grant $100K MITACS Grant $50K Google Grant Small part of $5.5M Alberta Transplant Institute Small part of $1M Polyomix Grant Facilities 68 processor, 2TB, Opteron cluster 54 processor, dual core, 1.5 TB, Opteron cluster

10. 10 Partners: Alberta Transplant Institute Cross Cancer Institute University of Waterloo University of Michigan University of Munich National ICT Australia Google

11. 11 Bicluster coding Dramatic improvement to cancer patient prognosis from microarrays: 62%  86% SVM accuracy Action respecting embedding Major impact in mobile robotics community “ ARE is the most interesting use of dimensionality reduction I have seen ’’ – leading expert

12. Technical Details

13. 13 Construct a map from experience (SLAM), but … Can this be done entirely automatically using machine learning principles?

14. 14 Consider a toy problem: IMAGEBOT

15. 15 Consider a toy problem: IMAGEBOT Gets experience:

16. 16 What makes a good internal representation? Experience: Encoding: Good map: 1. Encoding has correct (low) dimensionality 2. Actions have simple representation in encoding

17. 17 Maximum Variance Unfolding (Saul, Weinberger, 2005) Maximize spread of data Preserve local distances Solves 1 but not 2

18. 18 Also need actions to have a simple representation Idea: add distance preserving constraints Distance preserving implies actions are linear in representation

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23. 23 Scaling Semidefinite programming is slow Can solve trajectories of length ~100 Continuous actions Obstacles or traversal costs State aliasing

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