Conductrics bandit basicsemetrics1016
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The document discusses optimization techniques for online experiments known as multi-armed bandits. It introduces the multi-armed bandit problem of balancing exploration of new options with exploitation of existing knowledge. It then describes several techniques for solving this problem, including AB testing, epsilon greedy, and upper confidence bound approaches. The document provides examples of how these techniques work and notes that multi-armed bandit methods can provide more efficient learning than AB testing alone.
Conductrics bandit basicsemetrics1016
- 1. Bandit Basics A Different take on Online Optimization Conductrics twitter: @mgershoff
- 2. Who is this guy? Matt Gershoff CEO: Conductrics Many Years in Database Marketing (New York and Paris) and a bit of Web Analytics www.conductrics.com twitter:@mgershoff Email:matt@conductrics.com
- 3. Speak Up Conductrics twitter: @mgershoff
- 4. What Are We Going to Hear? Optimization Basics Multi-Armed Bandit Its a Problem, Not a Method Some Methods AB Testing Epsilon Greedy Upper Confidence Interval (UCB) Some Results
- 5. Choices Targeting Learning Optimization Conductrics twitter: @mgershoff
- 6. OPTIMIZATION If THIS Then THAT ITTT brings together: 1.Decision Rules 2.Predictive Analytics 3.Choice Optimization Conductrics twitter: @mgershoff
- 7. OPTIMIZATION Find and Apply the Rule with the most Value If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT If THIS Then THAT Conductrics twitter: @mgershoff
- 8. OPTIMIZATION Variables whose Values Variables whose Values Are Given to You You Control THIS THAT If Facebook High Spend Urban GEO . Then Offer A Offer B Offer C . . Predictive Model . . . . F1 . . F2 S Valuei . Home Page Fm Offer Y App Use Offer Z Inputs Conductrics twitter: @mgershoff Outputs
- 9. But 1. We Dont Have Data on THAT 2. Need to Collect Sample THAT 3. How to Sample Efficiently? Offer A ? Offer B ? Offer C ? . . . . . Offer Y ? Offer Z ? Conductrics twitter: @mgershoff
- 10. Where Marketing Applications: Websites Mobile Social Media Campaigns Banner Ads Pharma: Clinical Trials Conductrics twitter: @mgershoff
- 11. What is a Multi Armed Bandit One Armed Bandit >Slot Machine The problem: How to pick between Slot Machines so that you walk out with most $$$ from Casino at the end of the Night? OR A B Conductrics twitter: @mgershoff
- 12. Objective Pick so as to get the most return/profit as you can over time Technical term: Minimize Regret Conductrics twitter: @mgershoff
- 13. Sequential Selection but how to Pick? OR A B Need to Sample, but do it efficiently Conductrics twitter: @mgershoff
- 14. Explore Collect Data OR A B Data Collection is costly an Investment Be Efficient Balance the potential value of collecting new data with exploiting what you currently know. Conductrics twitter: @mgershoff
- 15. Multi-Armed Bandits Bandit problems embody in essential form a conflict evident in all human action: choosing actions which yield immediate reward vs. choosing actions whose benefit will come only later.* - Peter Whittle *Source: Qing Zhao, UC Davis. Plenary talk at SPAWC, June, 2010. Conductrics twitter: @mgershoff
- 16. Exploration Exploitation 1) Explore/Learn Try out different actions to learn how they perform over time This is a data collection task. 2) Exploit/Earn Take advantage of what you have learned to get highest payoff Your current best guess Conductrics twitter: @mgershoff
- 17. Not A New Problem 1933 first work on competing options 1940 WWII Problem Allies attempt to tackle 1953 Bellman formulates as a Dynamic Programing problem Source: http://www.lancs.ac.uk/~winterh/GRhist.html Conductrics twitter: @mgershoff
- 18. Testing Explore First All actions have an equal chance of selection (uniform random). Use hypothesis testing to select a Winner. Then Exploit - Keep only Winner for selection Conductrics twitter: @mgershoff
- 19. Learn First Data Collection/Sample Apply Leaning Explore/ Exploit/ Learn Earn Time Conductrics twitter: @mgershoff
- 20. P-Values: A Digression P-Values: NOT the probability that the Null is True. P( Null=True| DATA) P(DATA (or more extreme)| Null=True) Not a great tool for deciding when to stop sampling See: http://andrewgelman.com/2010/09/noooooooooooooo_1/ http://www.stat.duke.edu/~berger/papers/02-01.pdf Conductrics twitter: @mgershoff
- 21. A Couple Other Methods 1. Epsilon Greedy Nice and Simple 2. Upper Confidence Bounds(UCB) Adapts to Uncertainty Conductrics twitter: @mgershoff
- 22. 1) Epsilon-Greedy Conductrics twitter: @mgershoff
- 23. Greedy What do you mean by Greedy? Make whatever choice seems best at the moment.
- 24. Epsilon Greedy What do you mean by Epsilon Greedy? Explore randomly select action percent of the time (say 20%) Exploit Play greedy (pick the current best) 1- (say 80%)
- 25. Epsilon Greedy User Explore/Learn Exploit/Earn (20%) (80%) Select Select Current Randomly Best Like AB Testing (Be Greedy) Conductrics twitter: @mgershoff
- 26. Epsilon Greedy 20% Random 80% Select Best Action Value A $5.00 B $4.00 C $3.00 D $2.00 E $1.00 Conductrics twitter: @mgershoff
- 27. Continuous Sampling Explore/Learn Exploit/Earn Time Conductrics twitter: @mgershoff
- 28. Epsilon Greedy Super Simple/low cost to implement Tends to be surprisingly effective Less affected by Seasonality Not optimal (hard to pick best ) Doesnt use measure of variance Should/How to decrease Exploration over time? Conductrics twitter: @mgershoff
- 29. Upper Confidence Bound Basic Idea: 1) Calculate both mean and a measure of uncertainty (variance) for each action. 2) Make Greedy selections based on mean + uncertainty bonus Conductrics twitter: @mgershoff
- 30. Confidence Interval Review Confidence Interval = mean +/- z*Std - 2*Std Mean +2*Std Conductrics twitter: @mgershoff
- 31. Upper Confidence Score each option using the upper portion of the interval as a Bonus Mean +Bonus Conductrics twitter: @mgershoff
- 32. Upper Confidence Bound 1) Use upper portion of CI as Bonus 2) Make Greedy Selections A Select A B C $0 $5 $10 Estimated Reward Conductrics twitter: @mgershoff
- 33. Upper Confidence Bound 1) Selecting Action A reduces uncertainty bonus (because more data) 2) Action C now has highest score A B C Select C $0 $5 $10 Estimated Reward Conductrics twitter: @mgershoff
- 34. Upper Confidence Bound Like A/B Test uses variance measure Unlike A/B Test no hypothesis test Automatically Balances Exploration with Exploitation Conductrics twitter: @mgershoff
- 35. Case Study: Conversion Treatment Rate Served V2V3 9.9% 14,893 V2V2 9.7% 9,720 V2V1 8.0% 2,441 V1V3 3.3% 2,090 V2V3 2.6% 1,849 V2V2 2.0% 1,817 V1V1 1.8% 1,926 V3V1 1.8% 1,821 V1V2 1.5% 1,873 Conductrics twitter: @mgershoff
- 36. Case Study Test Method Conversion Rate Adaptive 7% Non Adaptive 4.5% Conductrics twitter: @mgershoff
- 37. AB Testing V Bandit Option A -> Option B -> Option C -> Conductrics twitter: @mgershoff
- 38. Why Should I Care? More Efficient Learning Automation Changing World Conductrics twitter: @mgershoff
- 39. Questions? Conductrics twitter: @mgershoff
- 40. Thank You! Matt Gershoff p) 646-384-5151 e) matt@conductrics.com t) @mgershoff Conductrics twitter: @mgershoff