This set of slides describes several on-line learning algorithms which taken together can provide significant benefit to real-time applications.

1. Online Learning
Bayesian bandits and more
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2. whoami – Ted Dunning
 Ted Dunning
tdunning@maprtech.com
tdunning@apache.org
@ted_dunning
 We’re hiring at MapR
 For slides and other info
http://www.slideshare.net/tdunning
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3. Online
Scalable
Incremental
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4. Scalability and Learning
 What does scalable mean?
 What are inherent characteristics of scalable learning?
 What are the logical implications?
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5. Scalable ≈ On-line
If you squint just right
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6. unit of work ≈ unit of time
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7. Infinite
Data Learning
Stream
State
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8. Pick One
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9. ©MapR Technologies - Confidential 9

10. ©MapR Technologies - Confidential 10

11. Now pick again
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12. A Quick Diversion
 You see a coin
– What is the probability of heads?
– Could it be larger or smaller than that?
 I flip the coin and while it is in the air ask again
 I catch the coin and ask again
 I look at the coin (and you don’t) and ask again
 Why does the answer change?
– And did it ever have a single value?
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13. Which One to Play?
 One may be better than the other
 The better coin pays off at some rate
 Playing the other will pay off at a lesser rate
– Playing the lesser coin has “opportunity cost”
 But how do we know which is which?
– Explore versus Exploit!
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14. A First Conclusion
 Probability as expressed by humans is subjective and depends on
information and experience
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15. A Second Conclusion
 A single number is a bad way to express uncertain knowledge
 A distribution of values might be better
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16. I Dunno
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17. 5 and 5
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18. 2 and 10
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19. The Cynic Among Us
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20. Demo
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21. An Example
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22. An Example
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23. The Cluster Proximity Features
 Every point can be described by the nearest cluster
– 4.3 bits per point in this case
– Significant error that can be decreased (to a point) by increasing number of
clusters
 Or by the proximity to the 2 nearest clusters (2 x 4.3 bits + 1 sign
bit + 2 proximities)
– Error is negligible
– Unwinds the data into a simple representation
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24. Diagonalized Cluster Proximity
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25. Lots of Clusters Are Fine
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26. Surrogate Method
 Start with sloppy clustering into κ = k log n clusters
 Use these clusters as a weighted surrogate for the data
 Cluster surrogate data using ball k-means
 Results are provably high quality for highly clusterable data
 Sloppy clustering can be done on-line
 Surrogate can be kept in memory
 Ball k-means pass can be done at any time
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27. Algorithm Costs
 O(k d log n) per point for Lloyd’s algorithm
… not so good for k = 2000, n = 108
 Surrogate methods
…. O(d log κ) = O(d (log k + log log n)) per point
 This is a big deal:
– k d log n = 2000 x 10 x 26 = 500,000
– log k + log log n = 11 + 5 = 17
– 30,000 times faster makes the grade as a bona fide big deal
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28. 30,000 times faster sounds good
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29. 30,000 times faster sounds good
but that isn’t the big news
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30. 30,000 times faster sounds good
but that isn’t the big news
these algorithms do
on-line clustering
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31. Parallel Speedup?
200
Non- threaded
✓
100
2
Tim e per point (μs)
Threaded version
3
50
4
40 6
5
8
30
10 14
12
20 Perfect Scaling 16
10
1 2 3 4 5 20
Threads
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32. What about deployment?
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33. Infinite
Data Learning
Stream
State
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34. Data
Mapper
Split
State
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35. Data
Mapper
Mapper
Split Mapper
Need shared
memory! State
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36. whoami – Ted Dunning
 We’re hiring at MapR
 Ted Dunning
tdunning@maprtech.com
tdunning@apache.org
@ted_dunning
 For slides and other info
http://www.slideshare.net/tdunning
©MapR Technologies - Confidential 36