Michael Jordan, professor at UC Berkeley, presents empirical benchmark results comparing his Bag of Little Bootstraps algorithm against standard bootstrap resampling on terabyte-scale datasets using Amazon EC2 and Apache Spark.
“Here's the new algorithm, you know, again, implement on Spark. It's that little red box there. It took about a couple hundred seconds to get it, And the answer quality is better than the bootstrap after 15,000 seconds.”
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More from Michael Jordan
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Jordan predicts principled personalized big data systems remain decades away
“So I think we're decades away from being able to do what this boss is asking us to do in some principle way. You can occasionally build a one-off system that does some of these things, but we're decades from having the real principles.”
Jordan says personalization business models fail due to statistical limits
“A lot of these business models are failing. People actually can't personalize very well, and it's Because of statistical issues. You've got huge amounts of data about some people, and very little about lots of people, and you don't know how to transfer the sta…”
Jordan warns high-dimensional Bayesian inference is overly sensitive to unknown priors
“And a lot of times you have no idea what the prior should be. You don't know what the tails should be in particular. And you're in high dimensions, you really have no idea how the tail behavior should be. And the whole inference is highly sensitive to the tail…”
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