Real Time Learning
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Transcript of Real Time Learning
1©MapR Technologies - Confidential
Real-time Learning
2©MapR Technologies - Confidential
Contact:– [email protected]– @ted_dunning
Slides and such (available late tonight):– http://slideshare.net/tdunning
Hash tags: #mapr #hivedata
3©MapR Technologies - Confidential
We have a product to sell … from a web-site
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What picture?
What tag-line?
What call to action?
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The Challenge
Design decisions affect probability of success– Cheesy web-sites don’t even sell cheese
The best designers do better when allowed to fail– Exploration juices creativity
But failing is expensive– If only because we could have succeeded– But also because offending or disappointing customers is bad
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More Challenges
Too many designs– 5 pictures– 10 tag-lines– 4 calls to action– 3 back-ground colors=> 5 x 10 x 4 x 3 = 600 designs
It gets worse quickly– What about changes on the back-end?– Search engine variants?– Checkout process variants?
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Example – AB testing in real-time
I have 15 versions of my landing page Each visitor is assigned to a version– Which version?
A conversion or sale or whatever can happen– How long to wait?
Some versions of the landing page are horrible– Don’t want to give them traffic
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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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A Philosophical Conclusion
Probability as expressed by humans is subjective and depends on information and experience
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I Dunno
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5 heads out of 10 throws
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2 heads out of 12 throws
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So now you understand Bayesian probability
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Another Quick Diversion
Let’s play a shell game This is a special shell game It costs you nothing to play The pea has constant probability of being under each shell
(trust me) How do you find the best shell? How do you find it while maximizing the number of wins?
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Pause for short con-game
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Interim Thoughts
Can you identify winners or losers without trying them out?
Can you ever completely eliminate a shell with a bad streak?
Should you keep trying apparent losers?
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Pause for second con-game
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So now you understand multi-armed bandits
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Conclusions
Can you identify winners or losers without trying them out?No
Can you ever completely eliminate a shell with a bad streak?No
Should you keep trying apparent losers?Yes, but at a decreasing rate
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Is there an optimum strategy?
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Bayesian Bandit
Compute distributions based on data so far Sample p1, p2 and p2 from these distributions
Pick shell i where i = argmaxi pi
Lemma 1: The probability of picking shell i will match the probability it is the best shell
Lemma 2: This is as good as it gets
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And it works!
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Video Demo
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The Code
Select an alternative
Select and learn
But we already know how to count!
n = dim(k)[1] p0 = rep(0, length.out=n) for (i in 1:n) { p0[i] = rbeta(1, k[i,2]+1, k[i,1]+1) } return (which(p0 == max(p0)))
for (z in 1:steps) { i = select(k) j = test(i) k[i,j] = k[i,j]+1 } return (k)
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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
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The Original Problem
x1x2
x3
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Response Function
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Generalized Banditry
Suppose we have an infinite number of bandits– suppose they are each labeled by two real numbers x and y in [0,1]– also that expected payoff is a parameterized function of x and y
– now assume a distribution for θ that we can learn online Selection works by sampling θ, then computing f Learning works by propagating updates back to θ– If f is linear, this is very easy
Don’t just have to have two labels, could have labels and context
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Context Variables
x1x2
x3
user.geo env.time env.day_of_week env.weekend
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Caveats
Original Bayesian Bandit only requires real-time
Generalized Bandit may require access to long history for learning– Pseudo online learning may be easier than true online
Bandit variables can include content, time of day, day of week
Context variables can include user id, user features
Bandit × context variables provide the real power
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You can do thisyourself!
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Thank You
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Contact:– [email protected]– @ted_dunning
Slides and such (available late tonight):– http://slideshare.net/tdunning
Hash tags: #mapr #hivedata