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So what happened with those goofy numbers? The researchers worked with a single survey, and surveys have sampling error. When, after an election is over, you use a survey to summarize election results, standard practice is to adjust to match the sample with the known vote totals: that’s what the exit polls do, and that’s what do too. If you don’t make that adjustment, you get numbers that don’t add up to what actually happened.

Or maybe I just messed something up in my calculations. That’s an advantage of blogging—if I get something wrong, one of you is likely to correct me. A newspaper op-ed just sits there, with no one really taking responsibility for its correctness. This one looks so wrong, that I do think there’s a good chance that I’m just missing something obvious. If so, please let me know.

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Filed under Causal Inference , Political Science .
Posted by Andrew on 3 July 2018, 9:02 am

Kaiser Fung points to this news article by David Jackson and Gary Marx:

The Illinois Department of Children and Family Services is ending a high-profile program that used computer data mining to identify children at risk for serious injury or death after the agency’s top official called the technology unreliable. . . .

Two Florida firms — the nonprofit Eckerd Connects and its for-profit partner, Mindshare Technology — mined electronic DCFS files and assigned a score of 1 to 100 to children who were the subject of an abuse allegation to the agency hotline. The algorithms rated the children’s risk of being killed or severely injured during the next two years, according to DCFS public statements.

OK, this could work. But then:

More than 4,100 Illinois children were assigned a 90 percent or greater probability of death or injury . . . 369 youngsters, all under age 9, got a 100 percent chance of death or serious injury in the next two years . . . At the same time, high-profile child deaths kept cropping up with little warning from the predictive analytics software . . . The DCFS automated case-tracking system was riddled with data entry errors . . .

Illinois child care agencies told the Tribune they were alarmed by computer-generated alerts like the one that said: “Please note that the two youngest children, ages 1 year and 4 years have been assigned a 99% probability by the Eckerd Rapid Safety Feedback metrics of serious harm or death in the next two years.”

Check out this response:

“We all agree that we could have done a better job with that language. I admit it is confusing,” said Eckerd spokesman Douglas Tobin.

Ummm . . . “confusing”? That’s all you can say? How about, “We screwed up. Our numbers were entirely wrong.”

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Sarah Rockett

Through this series, local creatives from a variety of disciplines push the notions of what a museum can be—for visitors, for the creative community and for the institution itself. Things will get wildly creative and maybe a little messy too!

Sarah Rockett, #winning, 2018. Mixed media sculptural wall installation; 192 x 312 x 24 in. Photo by Wes Magyar.

Sarah Rockett, Pod People, 2016. Faux fur, silicone, lights, used business shoes, astroturf; 40 x 96 x 72 in. Photo by Wes Magyar.

Sarah Rockett, Gold Spoons Project, 2016. Metal leaf, used spoons; dimensions variable. Photo by Sarah Rockett.

Sarah Rockett is a socially engaged, interdisciplinary artist, and educator living in Denver. Her work focuses on social commentary by investigating value and equality in American society. Through her work, Rockett, highlights the value of art in society through various sociopolitical lenses. Her spirit for community building is inspired by her time in New Orleans and the destruction caused by Hurricane Katrina. As such, Rockett’s desire to illuminate social intersections is prevalent in her own work and in her dedication to collaborative projects.

As the museum’s Creative-in-Residence, Rockett will be using art as a method for engaging social interaction, creating meaningful human connection, and prompting dialogue around the urban housing crisis in Denver. Through exploring ideas of “home” and “place,” museum visitors are invited to join Rockett in the making process and participate in dialogue about the subject.

“Art can be used as a mechanism to initiate dialogue around important issues and propagate change.” – Sarah Rockett

While in residence at the DAM, Rockett will be inviting museum visitors to participate in a collaborative project. Sit down with Rockett - immersed in the DAM’s current exhibitions on view - and learn how to embroider or practice your tried skill, while connecting with Rockett and others in dialogue surrounding Denver’s urban housing crisis. Throughout the summer, Rockett will be inviting in community service leaders involved in Denver’s urban housing crisis to participate alongside visitors in creation and dialogue.

In July, August, and September find Rockett working in the galleries on Tuesdays and Thursdays from 1–3:30 pm, and the Free First Saturdays of August and September. Participate in Rockett’s project and hear from others about issues surrounding the urban housing crisis.

Learn more by follow Sarah Rockett on Instagram @sarahmrockett and her website:

About the Program

About the Program

Applications for 2018 have closed. We will be announcing our new Creative-in-Residence soon.

Through this series, local creatives from a variety of disciplines push the notions of what a museum can be—for visitors, for the creative community and for the institution itself. Things will get wildly creative and maybe a little messy too!

Get the latest updates on Facebook, Twitter and Instagram—use the hashtag #creativeinresidence.

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The Denver Art Museum received a grant from the Institute of Museum and Library Services to produce a series of creative-in-residence programs from March 2015–May 2016. These creatives have included a composer, a conceptual artist, a floral artist, and a hip hop group with each residency providing unexpected, surprising, and playful experiences. Black Felicity crystal heel 100 suede boots Sophia Webster 4zf1lnoh
(PDF) to discover more about this two-year project exploring how the art museum can work in collaboration with the local creative community to transform visitor experiences.

(Some work uses similar techniques to reduce high frequencies in the gradient before they accumulate in the visualization . These techniques are in some ways very similar to the above and in some ways radically different — we’ll examine them in the next section, Preconditioning and Parameterization .)

Frequency penalization directly targets high frequency noise

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Transformation robustness tries to find examples that still activate the optimization target highly even if we slightly transform them. Even a small amount seems to be very effective in the case of images , especially when combined with a more general regularizer for high-frequencies . Concretely, this means that we stochastically jitter, rotate or scale the image before applying the optimization step.

Stochastically transforming the image before applying the optimization step suppresses noise

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Learned priors. Our previous regularizers use very simple heuristics to keep examples reasonable. A natural next step is to actually learn a model of the real data and try to enforce that. With a strong model, this becomes similar to searching over the dataset. This approach produces the most photorealistic visualizations, but it may be unclear what came from the model being visualized and what came from the prior.

One approach is to learn a generator that maps points in a latent space to examples of your data, such as a GAN or VAE, and optimize within that latent space . An alternative approach is to learn a prior that gives you access to the gradient of probability; this allows you to jointly optimize for the prior along with your objective . When one optimizes for the prior and the probability of a class, one recovers a generative model of the data conditioned on that particular class. Finally, Wei et al. approximate a generative model prior, at least for the color distribution, by penalizing distance between patches of the output and the nearest patches retrieved from a database of image patches collected from the training data.

In the previous section, we saw a few methods that reduced high frequencies in the gradient rather than the visualization itself. It’s not clear this is really a regularizer: it resists high frequencies, but still allows them to form when the gradient consistently pushes for it. If it isn’t a regularizer, what does transforming the gradient like this do?

Transforming the gradient like this is actually quite a powerful tool — it’s called “preconditioning” in optimization. You can think of it as doing steepest descent to optimize the same objective, but in another parameterization of the space or under a different notion of distance. Gradient blurring is equivalent to gradient descent in a different parameterization of image space, where high frequency dimensions are stretched to make moving in those directions slower. Gradient Laplacian Pyramid normalization is a kind of adaptive learning rate approach in the same space. This changes which direction of descent will be steepest, and how fast the optimization moves in each direction, but it does not change what the minimums are. If there are many local minima, it can stretch and shrink their basins of attraction, changing which ones the optimization process falls into. As a result, using the right preconditioner can make an optimization problem radically easier.