Lahug 2012-02-07

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A corrected set of slides from the LA HUG talk that I gave in February 2012

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  • No information would give a relative expected payoff of -0.25. This graph shows 25, 50 and 75%-ile results for sampled experiments with uniform random probabilities. Convergence to optimum is nearly equal to the optimum sqrt(n). Note the log scale on number of trials
  • Here is how the system converges in terms of how likely it is to pick the better bandit with probabilities that are only slightly different. After 1000 trials, the system is already giving 75% of the bandwidth to the better option. This graph was produced by averaging several thousand runs with the same probabilities.
  • Lahug 2012-02-07

    1. 1. Beating up on Bayesian Bandits
    2. 2. Mahout• Scalable Data Mining for Everybody
    3. 3. What is Mahout• Recommendations (people who x this also x that)• Clustering (segment data into groups of)• Classification (learn decision making from examples)• Stuff (LDA, SVD, frequent item-set, math)
    4. 4. What is Mahout?• Recommendations (people who x this also x that)• Clustering (segment data into groups of)• Classification (learn decision making from examples)• Stuff (LDA, SVM, frequent item-set, math)
    5. 5. Classification in Detail• Naive Bayes Family – Hadoop based training• Decision Forests – Hadoop based training• Logistic Regression (aka SGD) – fast on-line (sequential) training
    6. 6. Classification in Detail• Naive Bayes Family – Hadoop based training• Decision Forests – Hadoop based training• Logistic Regression (aka SGD) – fast on-line (sequential) training
    7. 7. Classification in Detail• Naive Bayes Family – Hadoop based training• Decision Forests – Hadoop based training• Logistic Regression (aka SGD) – fast on-line (sequential) training – Now with MORE topping!
    8. 8. An Example
    9. 9. And AnotherFrom: Thu, Paul 20, 2010 at 10:51 AMDate: Dr. May AcquahDear Sir,From: George <george@fumble-tech.com>Re: Proposal for over-invoice Contract BenevolenceHi Ted, was a pleasure talking to you last nightBased on information gathered from the idea ofat the Hadoop User Group. I liked the Indiahospital directory, I am pleased to propose agoing for lunch together. Are you availableconfidential business noon? for our mutualtomorrow (Friday) at dealbenefit. I have in my possession, instruments(documentation) to transfer the sum of33,100,000.00 eur thirty-three million one hundredthousand euros, only) into a foreign companysbank account for our favor....
    10. 10. Feature Encoding
    11. 11. Hashed Encoding
    12. 12. Feature Collisions
    13. 13. How it Works• We are given “features” – Often binary values in a vector• Algorithm learns weights – Weighted sum of feature * weight is the key• Each weight is a single real value
    14. 14. 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?
    15. 15. A First Conclusion• Probability as expressed by humans is subjective and depends on information and experience
    16. 16. A Second Conclusion• A single number is a bad way to express uncertain knowledge• A distribution of values might be better
    17. 17. I Dunno
    18. 18. 5 and 5
    19. 19. 2 and 10
    20. 20. The Cynic Among Us
    21. 21. A Second Diversion
    22. 22. Two-armed Bandit
    23. 23. Which One to Play?• One may be better than the other• The better machine pays off at some rate• Playing the other will pay off at a lesser rate – Playing the lesser machine has “opportunity cost”• But how do we know which is which? – Explore versus Exploit!
    24. 24. Algorithmic Costs• Option 1 – Explicitly code the explore/exploit trade-off• Option 2 – Bayesian Bandit
    25. 25. Bayesian Bandit• Compute distributions based on data• Sample p1 and p2 from these distributions• Put a coin in bandit 1 if p1 > p2• Else, put the coin in bandit 2
    26. 26. The Basic Idea• We can encode a distribution by sampling• Sampling allows unification of exploration and exploitation• Can be extended to more general response models
    27. 27. Deployment with Storm/MapR Targeting Online Engine Model RPC RPC Model Selector RPC Online RPC Model Impression Logs Training Conversion Online Training Detector Model Training Click Logs RPC All state managed transactionally in MapR file system Conversion Dashboard
    28. 28. Service Architecture MapR Pluggable Service Management StormTargeting Online Engine Model RPC RPC Model Selector RPC OnlineImpression Logs Conversion Detector RPC Training Training Model Online Hadoop Model TrainingClick Logs RPCConversionDashboard MapR Lockless Storage Services
    29. 29. Find Out More• Me: tdunning@mapr.com ted.dunning@gmail.com tdunning@apache.com• MapR: http://www.mapr.com• Mahout: http://mahout.apache.org• Code: https://github.com/tdunning

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