Presenter: Dale Schuurmans. 2 Helping the world understand data and make informed decisions Core technology research: Underlies prediction and control,

Learning Learning Models from Models from

DataDataPresenter: Dale SchuurmansPresenter: Dale Schuurmans

2

Vision StatementVision Statement

Helping the world understand data and make informed decisions

Core technology research:• Underlies prediction and control,• Underlies most applications, • Involves almost every PI

3

MotivationMotivationAlmost 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

Discovering intrinsic structure in dataClasses

Manifolds

Components

Constraints

Dynamics

(Usually unsupervised)

Learning a ModelLearning a Model

5

A General ThrustA General Thrust

Unifying challenge for AICMLUnderlies many other thrusts, applications

Fundamental researchNew principlesNew algorithmsNew ideas

AICML advantageCritical massOutstanding resourcesDiversity of applications

6

Projects and StatusProjects and Status

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

Projects and StatusProjects and Status

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

AICML Personnel (cumulative)AICML Personnel (cumulative)

Primary PI’s Goebel, Greiner, Holte, Bowling,

Schuurmans, Sutton, Szepesvari5 PDFs17 Grad students4 Technical staff

9

ResourcesResources

Grants$225K CFI New Opportunities Grant$100K MITACS Grant$50K Google GrantSmall part of $5.5M Alberta Transplant InstituteSmall part of $1M Polyomix Grant

Facilities68 processor, 2TB, Opteron cluster 54 processor, dual core, 1.5 TB, Opteron cluster

10

Partners/CollaboratorsPartners/Collaborators

Partners:Alberta Transplant InstituteCross Cancer InstituteUniversity of WaterlooUniversity of MichiganUniversity of MunichNational ICT AustraliaGoogle

11

Early Highlights Early Highlights

Bicluster codingDramatic improvement to cancer patient

prognosis from microarrays:62% 86% SVM accuracy

Action respecting embeddingMajor impact in mobile robotics community

“ARE is the most interesting use of dimensionality reduction I have seen’’ – leading expert

Action Action Respecting Respecting EmbeddingEmbedding

Technical DetailsTechnical Details

13

The ChallengeThe ChallengeConstruct a map from experience (SLAM), but …

Can this be done entirely automatically using machine learning principles?

14

The ChallengeThe ChallengeConsider a toy problem: IMAGEBOT

15

The ChallengeThe ChallengeConsider a toy problem: IMAGEBOT

Gets experience:

16

The ChallengeThe Challenge

What makes a good internal representation?

Experience:

Encoding:

Good map:1. Encoding has correct (low) dimensionality2. Actions have simple representation in encoding

17

Basic Dimensionality ReductionBasic Dimensionality Reduction

Maximum Variance Unfolding(Saul, Weinberger, 2005)

Maximize spread of dataPreserve local distances

Solves 1 but not 2

18

ApproachApproach

Also need actions to have a simple representationIdea: add distance preserving constraints

Distance preserving implies actions are linear in representation

19

ResultsResults

20

ResultsResults

21

ResultsResults

22

ConclusionsConclusions

23

The FutureThe Future

ScalingSemidefinite programming is slowCan solve trajectories of length ~100

Continuous actionsObstacles or traversal costsState aliasing

24

Questions?Questions?