We’ve just finished off a series of articles on some recent research results applying differential privacy to improve machine learning. Some of these results are pretty technical, so we thought it was worth working through concrete examples. And some of the original results are locked behind academic journal paywalls, so we’ve tried to touch on the highlights of the papers, and to play around with variations of our own.
A Simpler Explanation of Differential Privacy: Quick explanation of epsilon-differential privacy, and an introduction to an algorithm for safely reusing holdout data, recently published in Science (Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth, “The reusable holdout: Preserving validity in adaptive data analysis”, Science, vol 349, no. 6248, pp. 636-638, August 2015).
Note that Cynthia Dwork is one of the inventors of differential privacy, originally used in the analysis of sensitive information.
Using differential privacy to reuse training data: Specifically, how differential privacy helps you build efficient encodings of categorical variables with many levels from your training data without introducing undue bias into downstream modeling.
As readers have surely noticed the Win-Vector LLCblog isn’t a stream of short notes, but instead a collection of long technical articles. It is the only way we can properly treat topics of consequence.
What not everybody may have noticed is a number of these articles are serialized into series for deeper comprehension. The key series include:
Statistics to English translation.
This series tries to find vibrant applications and explanations of standard good statistical practices, to make them more approachable to the non statistician.
Statistics as it should be.
This series tries to cover cutting edge machine learning techniques, and then adapt and explain them in traditional statistical terms.
R as it is.
This series tries to teach the statistical programming language R “warts and all” so we can see it as the versatile and powerful data science tool that it is.
Win-Vector LLC’s Nina Zumel has a great new article on the issue of taste in design and problem solving: Design, Problem Solving, and Good Taste. I think it is a big issue: how can you expect good work if you can’t even discuss how to tell good from bad?
Nina Zumel also examines aspects of the supernatural in literature and in folk culture at her blog, multoghost.wordpress.com. She writes about folklore, ghost stories, weird fiction, or anything else that strikes her fancy. Follow her on Twitter @multoghost.