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Suresh Venkatasubramanian
Works at U. Utah
Attended Stanford
Lives in Salt Lake City
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I'm at IIIT Allahabad teaching a short course on fairness, accountability and transparency in machine learning. This is part of the GIAN pr...
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Shriram Krishnamurthi's profile photoAdam Smith's profile photoSuresh Venkatasubramanian's profile photo
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yes I've been browsing that for inspiration :)
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A (brief) musing on modeling.
Today, I got into a nice twitter discussion with former colleague and NLPer extraordinaire (even when he hates on algorithms ) Hal Daumé . ...
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This should be good.
 
Extracting structure from data is considered by many to be the frontier of machine learning. Yet even defining "structure", or the very goal of learning structure from unlabelled data, is not well understood or rigorously def...
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Sorelle Friedler, Carlos Scheidegger and I j ust posted a new paper on the arxiv where we try to lay out a framework for talking about fair...
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Moritz tackles a very important issue in this post: defining goals for fairness in Machine Learning. It's trickier than it seems, but a very important topic.
Many have given up on the very concept, often arguing that machine learning isn't biased, but merely mirrors our own biases by fitting models to 'naturally occurring' biased data. That's simply not true: machine learning applied without care is inherently biased toward where there is a lot of data in the first place: think groups of people who consume more, or ethnicities that show up more often on medical records. It is something we can control and characterize however, as long as we have the proper definitions in place of what it means to a probabilistic model to be 'fair'.
As machine learning increasingly affects domains protected by anti-discrimination law, there is much interest in the problem of algorithmically measuring and...
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And he did it!
+John Moeller
My student +John Moeller (moeller.fyi) just defended his Ph.D thesis today! and yes, there was a (rubber) snake-fighting element to the defense. John's dissertation work is in machine learning, but his publications span a wider range. He started off with a rather hard problem: attempting to ...
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The other day Geoff Challen posted a blog entry about his negative tenure vote. Having spent roughly equal time on the getting-tenure and having-tenure sides of the table, I wanted to comment on the process a little. Before going any further I want to clarify that: I know Geoff, but not well ...
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The SOCG 2017 call for papers is out. 300-word abstracts are due November 28; papers are due December 5.



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As part of the research we're doing in algorithmic fairness we're looking to hire a post-doctoral researcher who can help us bridge the gap between the more technical aspects of algorithmic fairness and the ways in which this discussion informs and is informed by the larger context in the social science.

+Sorelle Friedler

https://algorithmicfairness.wordpress.com/2016/10/03/post-doc-in-fairness-at-data-and-society/
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Indeed. Well we can define "expertise" broadly :). And I've met a number of people who actually do navigate the space reasonably well, which is to say that they can converse with a cs person and also point me to literature/people on the SS side. Thanks for forwarding the ad to them !!
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21st century living, in a nutshell.
A philosophy webcomic about the inevitable anguish of living a brief life in an absurd world. Also Jokes
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What's also nice about his post is the distinction between information obtained from you directly and information obtained about you.

DP doesn't protect the latter. And his complaint is in how people expect it should.

While this is true,I think it's a distinction that has taken some time to emerge (because privacy is such a slippery notion) and in some ways DP has helped emphasize this distinction. So a possibly better line of "attack" as it were might be to spell this out more clearly (if it already hasn't been done so) instead of playing a valiant but losing game of whack-a-paper :)
 
Another great post by Frank McSherry resolving some misconceptions about the guarantees of differential privacy under correlations between data entries. My favorite passage:

"By way of analogy, we use cryptography to protect our financial information, even though there is probably lots of correlation among our bank accounts. If I see you moving into your new house and call to ask about your mortgage, because often people with new houses have mortgages, have we violated the security guarantees of the cryptosystem your bank uses? Is it now time to write a paper: "cryptography vulnerable to correlated data", announcing that to provide its guarantees, modern cryptography assumes all messages are independent? Please don't."

https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-29.md
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I hadn't seen this response to the McSherry et al criticisms of the paper trashing differential privacy. +Moritz Hardt, +Aaron Roth, +Anand Sarwate any thoughts?

http://blogs.harvard.edu/infolaw/2016/05/17/diffensive-privacy/
A Response to the Criticisms of Fool's Gold: An Illustrated Critique of Differential Privacy. By Jane Bambauer and Krish Muralidhar. Two years ago, we coauthored an article that challenged the popular enthusiasm for Differential Privacy. Differential Privacy is a technique that permits ...
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Suresh's Collections
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I like algorithms. And I hope they're fair.
Introduction
CS prof, interested in algorithms, geometry, data mining, clustering
Education
  • Stanford
  • IIT Kanpur
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Male
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geomblog
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  • U. Utah
    Associate professor, present
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Currently
Salt Lake City
Previously
Aarhus, DK - Stanford, CA - Philadelphia, PA - Morristown, NJ - New Delhi, India - Berkeley, CA
That this restaurant has an overall rating less than 4 is a travesty. Skip Finca's overrated food and preserve your ear drums: Cafe Madrid is a much more intimate (read: quiet and charming) Spanish fine dining experience, with possibly the best service I've ever had in Salt Lake City. Call ahead if you want the paella: it's worth it.
Public - a year ago
reviewed a year ago
3 reviews
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