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Perfect Timing

This may be just what I needed:

Dear Abraham,

I am pleased to announce that Foundations and Trends in Machine Learning ( has published the following issue:

Volume 9, Issue 2-3
Patterns of Scalable Bayesian Inference
By Elaine Angelino (University of California, Berkeley, USA), Matthew James Johnson (Harvard University, USA) and
Ryan P. Adams (Harvard University and Twitter, USA)

The link will take you to the article abstract. If your library has a subscription, you will be able to download the PDF of the article.
If you do not have access, download the free preview here:

To purchase the book version of this issue, go to the secure Order Form:
You will receive the alert member discount price of $40 (includes shipping) by quoting the Promotion Code: 318306

This issue is also available for purchase at this year’s NIPS conference. Visit our booth to view all of the latest FnT ML titles.

Best Regards,

Tanya Capawana
now publishers

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Einstein’s Obituary for Emmy Noether

A children’s book on women in STEM led me to this gem:

[get nytimes screenshot, possibly tell story about using privilege, and the sooner the better[

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A problem with measuring mortality

Preparing for the release of our paper of leading causes of death in US counties led me to review the origins of a quote often attribute to Stalin:

Lots to unpack there. The important thing for me is remember that there are individual human tragedies behind the 80 million data points in our study. Respect.

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Advice from Abett

Abett is my former colleague Mike Hanlon’s new startup, and its advice app is now publicly available.

I remember fondly the days I spent working with Mike, who has a wealth of stories from has time as employee #7 at Amazon. He was in the cruicble as AMZN formed, stormed, and normed, and I got a lot of insight into what IHME’s fast growth could look like from his battle tales.

He has lofty goals for Abett as well, to use “big data”, in this case meaning user trace data, to help _people_ instead of advertisers. I’m 100% for that.


There is no advertising on our service, and never will be. We don’t care which products you buy, or if you buy any at all. We want you to make the right decision for you. Advertising would bias that objectivity, and thus we don’t accept it.

For more:

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Non-associativity of floating point addition and why testing scientific Python is hard

I’m lecturing Python II for a Software Carpentry Bootcamp in Jan, and I thought I’d find a little example of a funny fact I’ve heard: IEEE floating point addition does not obey the associative law.

I must be spending too much time with doctors, because I didn’t try to make an example myself and started by looking it up in Google. The first example I found put it cleanly:

>>> x=(0.1+0.2)+0.3
>>> y=0.1+(0.2+0.3)
>>> x==y
>>> print('%.17f' %x)
>>> print('%.17f' %y)

This shows the rumor is true: addition is not associative. It does not seem like a big deal, though, since I usually round my numbers to one or two significant digits, and I know how to test with `np.allclose`.

The second example I found makes the problem clearer, though:

x = (17 + 1e32) - 1e32
y = 17 + (1e32 - 1e32)

Can’t `np.allclose` that, unless you know what “close” means…

Additional reading:

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Autopilot off — here is something more current

The stream of content for the last week was all pre-scheduled. Here is something I wrote more recently, to my colleagues on the Diversity Committee in UW Dept. of Global Health:

From: Abraham D. Flaxman
Sent: Tuesday, November 15, 2016 12:06 PM
To: dghdiversity
Subject: Reflecting on Recent Events in American Politics

Dear DGH Diversity Committee,

It has taken a week for me to start writing a response to the recent turn in US politics. I’m barely ready to start now, but I also can’t wait to say something.

I don’t know who among us voted, or how, but in our department and in our work, it is important to remember that we don’t all have the same views. We don’t all have the same access or power, either. Many of our colleagues are not able to vote in US elections, despite how dramatically the results will affect them. This is profoundly unfair.

Many of those who are allowed to vote chose not to. My brother estimated that around 50% of voting-age citizens did not vote for Clinton or Trump last week.

Time will tell which of President-Elect Trump’s campaign promises were serious and which were “opening bids for negotiation”. Regardless, something is different this week. The extremist views put forth by Trump during the worst moments of this campaign have been validated by our electoral process. I hoped that when the votes were counted it would repudiate xenophobia and racism, reject sexual assault, and basically just stand up against bullying. Far from hope.

Instead, our nastiest tendencies are now elevated. It may seem that we have license to act worse towards each other than last week. As a diversity committee, department, and world, we must work to counter this. In ourselves, in our colleagues, and in our students.


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SIAM Talk on Reproducibility

> Victoria Stodden gave a talk on “Implementing Reproducibility in Computational Science” at the SIAM Annual meeting this summer — audio and slides are available at

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