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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/jel.53.3.631"/>
	<rdf:li rdf:resource="https://ohshitgit.com/"/>
	<rdf:li rdf:resource="https://ggdag.netlify.com/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1805.12462"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1706.02744"/>
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	<rdf:li rdf:resource="http://www.powells.com/biblio/9780465065707"/>
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	<rdf:li rdf:resource="http://www.andrewalexanderprice.com/blog20131204.php"/>
	<rdf:li rdf:resource="https://www.princeton.edu/~rvan/ORF570.pdf"/>
	<rdf:li rdf:resource="http://mlg.eng.cam.ac.uk/yarin/extrapolated-art.html"/>
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	<rdf:li rdf:resource="http://www.math.psu.edu/morton/publications/pcca.pdf"/>
	<rdf:li rdf:resource="https://www.techdirt.com/articles/20121226/17582221493/patent-trolling-carnegie-mellon-wins-what-could-be-largest-patent-verdict-ever-12-billion.shtml"/>
	<rdf:li rdf:resource="http://www.exp-platform.com/Pages/PuzzingOutcomesExplained.aspx"/>
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	<rdf:li rdf:resource="http://www.nicebread.de/visually-weighted-regression-in-r-a-la-solomon-hsiang/"/>
	<rdf:li rdf:resource="http://httpcats.herokuapp.com/"/>
	<rdf:li rdf:resource="http://www.lrb.co.uk/v33/n21/pankaj-mishra/watch-this-man"/>
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	<rdf:li rdf:resource="http://www.johnmyleswhite.com/notebook/2010/08/17/unit-testing-in-r-the-bare-minimum/"/>
	<rdf:li rdf:resource="http://micromath.wordpress.com/2008/04/14/donald-knuth-calculus-via-o-notation/"/>
	<rdf:li rdf:resource="http://en.wikipedia.org/wiki/Phantom_of_Heilbronn"/>
	<rdf:li rdf:resource="http://www.globalpost.com/dispatch/pakistan/100219/taliban-pakistan-baluchistan?page=0,0"/>
	<rdf:li rdf:resource="http://www.emis.de/journals/JEHPS/juin2009.html"/>
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  </channel><item rdf:about="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4912021/">
    <title>The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose is Not a Rose</title>
    <dc:date>2020-09-04T18:12:39+00:00</dc:date>
    <link>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4912021/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The assumption that exposures as measured in observational settings have clear and specific definitions underpins epidemiologic research and allows us to use observational data to predict outcomes in interventions. This leap between exposures as measured and exposures as intervened upon is typically supported by the consistency assumption. The consistency assumption has received extensive attention in risk factor epidemiology but relatively little emphasis in social epidemiology. However, violations of the consistency assumption may be especially important to consider when understanding how social and economic exposures influence health. Efforts to clarify the definitions of our exposures, thus bolstering the consistency assumption, will help guide interventions to improve population health and reduce health disparities. This article focuses on the consistency assumption as considered within social epidemiology. We explain how this assumption is articulated in the causal inference literature and give examples of how it might be violated for three common exposure in social epidemiology research: income, education and neighborhood characteristics. We conclude that there is good reason to worry about consistency assumption violations in much of social epidemiology research. Theoretically motivated explorations of mechanisms along with empirical comparisons of research findings under alternative operationalizations of exposure can help identify consistency violations. We recommend that future social epidemiology studies be more explicit to name and discuss the consistency assumption when describing the exposure of interest, including reconciling disparate results in the literature."]]></description>
<dc:subject>to:NB to_read causal_inference via:arsyed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:19232ff97c3b/</dc:identifier>
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<item rdf:about="https://link.springer.com/article/10.1007/s42113-018-0019-z">
    <title>Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection | SpringerLink</title>
    <dc:date>2020-09-01T12:04:20+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s42113-018-0019-z</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Discussions of model selection in the psychological literature typically frame the issues as a question of statistical inference, with the goal being to determine which model makes the best predictions about data. Within this setting, advocates of leave-one-out cross-validation and Bayes factors disagree on precisely which prediction problem model selection questions should aim to answer. In this comment, I discuss some of these issues from a scientific perspective. What goal does model selection serve when all models are known to be systematically wrong? How might “toy problems” tell a misleading story? How does the scientific goal of explanation align with (or differ from) traditional statistical concerns? I do not offer answers to these questions, but hope to highlight the reasons why psychological researchers cannot avoid asking them."]]></description>
<dc:subject>model_selection social_science_methodology via:arsyed statistics psychology in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c275d9352b65/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
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<item rdf:about="https://arxiv.org/abs/2001.01987">
    <title>[2001.01987] Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring</title>
    <dc:date>2020-03-18T17:56:52+00:00</dc:date>
    <link>https://arxiv.org/abs/2001.01987</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been analyzed empirically, but it has never before been mathematically derived. The softmax function partitions the transformed input space into cones, each of which encompasses a class. This is equivalent to putting a number of centroids in this transformed space at equal distance from the origin, and k-means clustering the data points by proximity to these centroids. Softmax only cares in which cone a data point falls, and not how far from the centroid it is within that cone. We formally prove that networks with a small Lipschitz modulus (which corresponds to a low susceptibility to adversarial attacks) map data points closer to the cluster centroids, which results in a mapping to a k-means-friendly space. To leverage this knowledge, we propose Centroid Based Tailoring as an alternative to the softmax function in the last layer of a neural network. The resulting Gauss network has similar predictive accuracy as traditional networks, but is less susceptible to one-pixel attacks; while the main contribution of this paper is theoretical in nature, the Gauss network contributes empirical auxiliary benefits."]]></description>
<dc:subject>to:NB neural_networks classifiers clustering k-means adversarial_examples via:arsyed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1f1d75e91ff6/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1901.00403">
    <title>[1901.00403] Can You Trust This Prediction? Auditing Pointwise Reliability After Learning</title>
    <dc:date>2019-09-27T17:29:17+00:00</dc:date>
    <link>https://arxiv.org/abs/1901.00403</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To use machine learning in high stakes applications (e.g. medicine), we need tools for building confidence in the system and evaluating whether it is reliable. Methods to improve model reliability often require new learning algorithms (e.g. using Bayesian inference to obtain uncertainty estimates). An alternative is to audit a model after it is trained. In this paper, we describe resampling uncertainty estimation (RUE), an algorithm to audit the pointwise reliability of predictions. Intuitively, RUE estimates the amount that a prediction would change if the model had been fit on different training data. The algorithm uses the gradient and Hessian of the model's loss function to create an ensemble of predictions. Experimentally, we show that RUE more effectively detects inaccurate predictions than existing tools for auditing reliability subsequent to training. We also show that RUE can create predictive distributions that are competitive with state-of-the-art methods like Monte Carlo dropout, probabilistic backpropagation, and deep ensembles, but does not depend on specific algorithms at train-time like these methods do."

--- I haven't read the paper, but I am going to now use this box to sketch how an idiot would tackle this problem.  (I do not mean that the authors are idiots.)  Since we're fitting our abyssal learning system by optimizing some loss function, the usual asymptotics for minimization apply (http://bactra.org/weblog/1017.html), and the variance matrix of the parameters $\theta$ is ($n$ times) the sandwich covariance matrix $h^{-1} j h^{-1}$, where $h$ is the Hessian of the loss function and $j$ is the covariance matrix of the gradient.  Now the prediction we make at point $x$ is $f(x;\theta)$.  This has some gradient w.r.t. the parameters at the point estimate, say $g(x)$.  Taylor-expand the prediction around the point estimate, stopping at first order.  Applying the usual algebra for variances tells us the variance of the prediction will be $g(x) \cdot n^{-1} h^{-1} j h^{-1} g(x)$.  This --- linearization plus variance algebra --- is "propagation of error" or "the delta method".
I am now going to make two predictions about the paper, which I have not read:
(1) The bit about "gradient and Hessian" in the abstract is a sign that they're talking about the sandwich covariance matrix.
(2) Their uncertainties-in-predictions are either propagation-of-error variances, _or_ they do not compare to to them.
If, on reading, I am wrong about either prediction, I will eat my crow here.

--- ETA after reading: OK, I need to eat a _little_ crow.  They assume the loss is a sum of IID point-by-point terms, meaning the gradient is too, and so the over-all loss gradient can be written as a sum of point-wise gradients, say $l_1, \ldots l_n$.  They then sample points with replacement (as in the bootstrap), and perturb the parameter estimate by a first-order Taylor series using the appropriate $l_i$'s.  (I'm not 100% sold on this step --- given that the influence of any one data point on the parameter estimate is small, still, replacing 1/3 of them isn't necessarily a local perturbation.)  Then they repredict with the new parameters, and take the variances of the repredictions over many resamplings.  (I don't see why --- they could just get a confidence interval for each prediction.) ]]></description>
<dc:subject>prediction statistics halbert_white_died_for_your_sins via:arsyed have_read uncertainty_for_neural_networks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:648d9693c602/</dc:identifier>
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</item>
<item rdf:about="https://arxiv.org/abs/1902.04783">
    <title>[1902.04783] Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning</title>
    <dc:date>2019-09-25T02:38:05+00:00</dc:date>
    <link>https://arxiv.org/abs/1902.04783</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impossible to satisfy all of them simultaneously. The literature so far has dealt with these impossibility results by quantifying the tradeoffs between different formulations of fairness. Our work takes a different perspective on this issue. Rather than requiring all notions of fairness to (partially) hold at the same time, we ask which one of them is the most appropriate given the societal domain in which the decision-making model is to be deployed. We take a descriptive approach and set out to identify the notion of fairness that best captures \emph{lay people's perception of fairness}. We run adaptive experiments designed to pinpoint the most compatible notion of fairness with each participant's choices through a small number of tests. Perhaps surprisingly, we find that the most simplistic mathematical definition of fairness---namely, demographic parity---most closely matches people's idea of fairness in two distinct application scenarios. This conclusion remains intact even when we explicitly tell the participants about the alternative, more complicated definitions of fairness, and we reduce the cognitive burden of evaluating those notions for them. Our findings have important implications for the Fair ML literature and the discourse on formalizing algorithmic fairness."

--- I worry that a lot will depend here on the interface / presentation...]]></description>
<dc:subject>algorithmic_fairness via:arsyed have_skimmed to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f6ef91f390fb/</dc:identifier>
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</item>
<item rdf:about="https://global.oup.com/academic/product/the-ethical-algorithm-9780190948207">
    <title>The Ethical Algorithm - Michael Kearns; Aaron Roth - Oxford University Press</title>
    <dc:date>2019-09-03T17:36:24+00:00</dc:date>
    <link>https://global.oup.com/academic/product/the-ethical-algorithm-9780190948207</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.
"Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology."]]></description>
<dc:subject>via:arsyed data_mining algorithmic_fairness kearns.michael to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination in_NB books:recommended</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc246869773a/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
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</item>
<item rdf:about="https://papers.nips.cc/paper/7620-modern-neural-networks-generalize-on-small-data-sets">
    <title>Modern Neural Networks Generalize on Small Data Sets</title>
    <dc:date>2018-12-09T14:29:18+00:00</dc:date>
    <link>https://papers.nips.cc/paper/7620-modern-neural-networks-generalize-on-small-data-sets</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we use a linear program to empirically decompose fitted neural networks into ensembles of low-bias sub-networks. We show that these sub-networks are relatively uncorrelated which leads to an internal regularization process, very much like a random forest, which can explain why a neural network is surprisingly resistant to overfitting. We then demonstrate this in practice by applying large neural networks, with hundreds of parameters per training observation, to a collection of 116 real-world data sets from the UCI Machine Learning Repository. This collection of data sets contains a much smaller number of training examples than the types of image classification tasks generally studied in the deep learning literature, as well as non-trivial label noise. We show that even in this setting deep neural nets are capable of achieving superior classification accuracy without overfitting."

--- If this has all just been an elaborate rediscovery of Krogh and Vedelsby (NIPS 1994), I may explode with exasperation/schadenfreude/delight.]]></description>
<dc:subject>to:NB learning_theory neural_networks via:arsyed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1beb711d5de8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jel.53.3.631">
    <title>Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern</title>
    <dc:date>2018-10-22T13:19:18+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jel.53.3.631</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of nonsampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each arises as the Bureau of Economic Analysis periodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with nonresponse, and the Bureau of Labor Statistics seasonally adjusts employment statistics. I anchor my discussion of communication of uncertainty in the contribution of Oskar Morgenstern (1963a), who argued forcefully for agency publication of error estimates for official economic statistics."]]></description>
<dc:subject>to:NB to_read statistics econometrics via:arsyed manski.charles_f.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:81cf90a2d0b7/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:manski.charles_f."/>
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</item>
<item rdf:about="https://ohshitgit.com/">
    <title>Oh, shit, git!</title>
    <dc:date>2018-08-13T13:29:01+00:00</dc:date>
    <link>https://ohshitgit.com/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>git version_control via:arsyed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b81fee7e0f82/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:git"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:version_control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ggdag.netlify.com/">
    <title>Analyze and Create Elegant Directed Acyclic Graphs • ggdag</title>
    <dc:date>2018-08-09T18:28:25+00:00</dc:date>
    <link>https://ggdag.netlify.com/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["ggdag: An R Package for visualizing and analyzing directed acyclic graphs"]]></description>
<dc:subject>R graphical_models visual_display_of_quantitative_information via:arsyed to_teach:undergrad-ADA re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3545bb27c7de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1805.12462">
    <title>[1805.12462] On GANs and GMMs</title>
    <dc:date>2018-06-08T02:41:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1805.12462</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A longstanding problem in machine learning is to find unsupervised methods that can learn the statistical structure of high dimensional signals. In recent years, GANs have gained much attention as a possible solution to the problem, and in particular have shown the ability to generate remarkably realistic high resolution sampled images. At the same time, many authors have pointed out that GANs may fail to model the full distribution ("mode collapse") and that using the learned models for anything other than generating samples may be very difficult. In this paper, we examine the utility of GANs in learning statistical models of images by comparing them to perhaps the simplest statistical model, the Gaussian Mixture Model. First, we present a simple method to evaluate generative models based on relative proportions of samples that fall into predetermined bins. Unlike previous automatic methods for evaluating models, our method does not rely on an additional neural network nor does it require approximating intractable computations. Second, we compare the performance of GANs to GMMs trained on the same datasets. While GMMs have previously been shown to be successful in modeling small patches of images, we show how to train them on full sized images despite the high dimensionality. Our results show that GMMs can generate realistic samples (although less sharp than those of GANs) but also capture the full distribution, which GANs fail to do. Furthermore, GMMs allow efficient inference and explicit representation of the underlying statistical structure. Finally, we discuss how a pix2pix network can be used to add high-resolution details to GMM samples while maintaining the basic diversity."

--- I wonder if I need a "your favorite deep learning technique/architecture sucks" tag.
--- ETA after being egged on: yes.  Yes I do.]]></description>
<dc:subject>to:NB neural_networks mixture_models high-dimensional_statistics via:arsyed your_favorite_deep_neural_network_sucks color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d4f345e1de1d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixture_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:your_favorite_deep_neural_network_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1706.02744">
    <title>[1706.02744] Avoiding Discrimination through Causal Reasoning</title>
    <dc:date>2017-11-14T17:07:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1706.02744</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. 
"Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them."]]></description>
<dc:subject>to_read causality algorithmic_fairness prediction machine_learning janzing.dominik re:ADAfaEPoV via:arsyed to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:25748940e755/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:janzing.dominik"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1709.02012">
    <title>[1709.02012] On Fairness and Calibration</title>
    <dc:date>2017-09-10T15:13:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.02012</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models, and this has motivated a growing line of work on what it means for a classification procedure to be "fair." In particular, we investigate the tension between minimizing error disparity across different population groups while maintaining calibrated probability estimates. We show that calibration is compatible only with a single error constraint (i.e. equal false-negatives rates across groups), and show that any algorithm that satisfies this relaxation is no better than randomizing a percentage of predictions for an existing classifier. These unsettling findings, which extend and generalize existing results, are empirically confirmed on several datasets."]]></description>
<dc:subject>to_read calibration prediction classifiers kleinberg.jon via:arsyed algorithmic_fairness to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9e16e63c8d3c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:calibration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kleinberg.jon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://openreview.net/forum?id=Sy8gdB9xx">
    <title>Understanding deep learning requires rethinking generalization</title>
    <dc:date>2017-02-08T21:07:31+00:00</dc:date>
    <link>https://openreview.net/forum?id=Sy8gdB9xx</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training.
"Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice.
"We interpret our experimental findings by comparison with traditional models."

--- This is very cool.]]></description>
<dc:subject>have_read neural_networks learning_theory statistics optimization to:blog via:arsyed in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cd3522739682/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1510.07389">
    <title>[1510.07389] The Human Kernel</title>
    <dc:date>2016-01-06T17:47:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1510.07389</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity. However, automating human expertise remains elusive; for example, Gaussian processes with standard kernels struggle on function extrapolation problems that are trivial for human learners. In this paper, we create function extrapolation problems and acquire human responses, and then design a kernel learning framework to reverse engineer the inductive biases of human learners across a set of behavioral experiments. We use the learned kernels to gain psychological insights and to extrapolate in human-like ways that go beyond traditional stationary and polynomial kernels. Finally, we investigate Occam's razor in human and Gaussian process based function learning."]]></description>
<dc:subject>to:NB kernel_methods nonparametrics regression statistics via:arsyed cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d0697aea3b53/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kernel_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.powells.com/biblio/9780465065707">
    <title>The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos - Powell's Books</title>
    <dc:date>2015-07-20T20:21:10+00:00</dc:date>
    <link>http://www.powells.com/biblio/9780465065707</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Please don't let this suck.]]></description>
<dc:subject>books:noted popular_science machine_learning domingos.pedro would_be_tagged_'to_be_shot_after_a_fair_trial'_if_it_were_by_almost_anyone_else via:arsyed in_wishlist</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f438b22a1b47/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:popular_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:domingos.pedro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:would_be_tagged_'to_be_shot_after_a_fair_trial'_if_it_were_by_almost_anyone_else"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_wishlist"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;arnumber=895563&amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D895563">
    <title>IEEE Xplore Abstract - Control theoretic smoothing splines</title>
    <dc:date>2014-09-30T12:35:04+00:00</dc:date>
    <link>http://ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;arnumber=895563&amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D895563</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Some of the relationships between optimal control and statistics are examined. We produce generalized, smoothing splines by solving an optimal control problem for linear control systems, minimizing the L2-norm of the control signal, while driving the scalar output of the control system close to given, prespecified interpolation points. We then prove a convergence result for the smoothing splines, using results from the theory of numerical quadrature. Finally, we show, in simulations, that our approach works in practice as well as in theory"]]></description>
<dc:subject>to:NB splines smoothing statistics via:arsyed control_theory_and_control_engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ef5eafb2c67c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:splines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:smoothing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.andrewalexanderprice.com/blog20131204.php">
    <title>A Traditional City Primer</title>
    <dc:date>2014-07-28T19:31:44+00:00</dc:date>
    <link>http://www.andrewalexanderprice.com/blog20131204.php</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Look, the eye-candy is great, but let's get real.  There were very powerful drives towards very large cities, in the form of economies of scale in production and infrastructure, and economies of agglomeration.  At the same time, living in a city of great size (say > 500k, a huge metropolis by pre-modern standards) without transit sucks.  Renaissance Florence --- a "traditional city" --- was counted very large at ~100k, or even ~70k, during its peak of prominence.  This is smaller than a modern college town like Ann Arbor or Madison, and comparable to a resort town like Santa Fe.  Show me this scaling up to even half a million and we'll talk.]]></description>
<dc:subject>cities urbanism design architecture via:arsyed have_read nostalgia via:arthegall</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:414298b353ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:urbanism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nostalgia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arthegall"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.princeton.edu/~rvan/ORF570.pdf">
    <title>Probability in High Dimension</title>
    <dc:date>2014-07-09T13:26:22+00:00</dc:date>
    <link>https://www.princeton.edu/~rvan/ORF570.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[2014 lecture notes for van Handel's class.  Looks great.]]></description>
<dc:subject>concentration_of_measure empirical_processes probability high-dimensional_probability learning_theory vc-dimension van_handel.ramon via:arsyed re:almost_none in_NB to_teach:childs_garden_of_statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f6d6eb8bb7ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:concentration_of_measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vc-dimension"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:van_handel.ramon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mlg.eng.cam.ac.uk/yarin/extrapolated-art.html">
    <title>extrapolated art - Cambridge Machine Learning Group | Yarin Gal</title>
    <dc:date>2014-04-28T13:33:03+00:00</dc:date>
    <link>http://mlg.eng.cam.ac.uk/yarin/extrapolated-art.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["New techniques in machine learning and image processing allow us to extrapolate the scene of a painting to see what the full scenery might have looked like. Click on a painting to extrapolate it – new paintings added every week. "

--- This sounds very like an idea Bill "Vaguery" Tozier had, in regards to http://bactra.org/weblog/000025.html  back in 2003...]]></description>
<dc:subject>art machine_learning spatial_statistics via:arsyed to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:10acc3611448/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rspb.royalsocietypublishing.org/content/281/1784/20133305">
    <title>Decision accuracy in complex environments is often maximized by small group sizes</title>
    <dc:date>2014-04-28T13:27:20+00:00</dc:date>
    <link>http://rspb.royalsocietypublishing.org/content/281/1784/20133305</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Individuals in groups, whether composed of humans or other animal species, often make important decisions collectively, including avoiding predators, selecting a direction in which to migrate and electing political leaders. Theoretical and empirical work suggests that collective decisions can be more accurate than individual decisions, a phenomenon known as the ‘wisdom of crowds’. In these previous studies, it has been assumed that individuals make independent estimates based on a single environmental cue. In the real world, however, most cues exhibit some spatial and temporal correlation, and consequently, the sensory information that near neighbours detect will also be, to some degree, correlated. Furthermore, it may be rare for an environment to contain only a single informative cue, with multiple cues being the norm. We demonstrate, using two simple models, that taking this natural complexity into account considerably alters the relationship between group size and decision-making accuracy. In only a minority of environments do we observe the typical wisdom of crowds phenomenon (whereby collective accuracy increases monotonically with group size). When the wisdom of crowds is not observed, we find that a finite, and often small, group size maximizes decision accuracy. We reveal that, counterintuitively, it is the noise inherent in these small groups that enhances their accuracy, allowing individuals in such groups to avoid the detrimental effects of correlated information while exploiting the benefits of collective decision-making. Our results demonstrate that the conventional view of the wisdom of crowds may not be informative in complex and realistic environments, and that being in small groups can maximize decision accuracy across many contexts."]]></description>
<dc:subject>to:NB to_read decision-making collective_cognition social_life_of_the_mind via:arsyed re:democratic_cognition couzin.iain entableted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8ec738acd6cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:couzin.iain"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:entableted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.math.psu.edu/morton/publications/pcca.pdf">
    <title>Principal Cumulant Component Analysis (Jason Morton, Lek-Heng Lim) [pdf]</title>
    <dc:date>2013-01-03T01:45:05+00:00</dc:date>
    <link>http://www.math.psu.edu/morton/publications/pcca.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Multivariate Gaussian data is completely characterized by its mean and covariance, yet modern non-Gaussian data makes higher-order statistics such as cumulants inevitable. For univariate data, the third and fourth scalar-valued cumulants are relatively well-studied as skewness and kurtosis. For multivariate data, these cumulants are tensor-valued, higher-order analogs of the covariance matrix capturing higher-order dependence in the data. In addition to their relative obscurity, there are few effective methods for analyzing these cumulant tensors. We propose a technique along the lines of Principal Component Analysis and Independent Component Analysis to analyze multivariate, non-Gaussian data motivated by the multilinear algebraic properties of cumulants. Our method relies on finding principal cumulant components that account for most of the variation in all higher-order cumulants, just as PCA obtains varimax components. An efficient algorithm based on limited-memory quasi-Newton maximization over a Grassmannian, using only standard matrix operations, may be used to find the principal cumulant components. Numerical experiments include forecasting higher portfolio moments and image dimension reduction."

- The to_teach tags here mean "to mention as further reading".]]></description>
<dc:subject>to:NB have_read statistics data_analysis cumulants via:arsyed principal_components to_teach:data-mining to_teach:undergrad-ADA dimension_reduction</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a056b61da10e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cumulants"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:principal_components"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dimension_reduction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.techdirt.com/articles/20121226/17582221493/patent-trolling-carnegie-mellon-wins-what-could-be-largest-patent-verdict-ever-12-billion.shtml">
    <title>Patent Trolling Carnegie Mellon Wins What Could Be Largest Patent Verdict Ever: $1.2 Billion | Techdirt</title>
    <dc:date>2012-12-28T22:01:31+00:00</dc:date>
    <link>https://www.techdirt.com/articles/20121226/17582221493/patent-trolling-carnegie-mellon-wins-what-could-be-largest-patent-verdict-ever-12-billion.shtml</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Well, this doesn't sound good for the school's moral health.]]></description>
<dc:subject>carnegie_mellon intellectual_property via:arsyed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0bf6e788901c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:carnegie_mellon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:intellectual_property"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.exp-platform.com/Pages/PuzzingOutcomesExplained.aspx">
    <title>Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained (Ron Kohavi)</title>
    <dc:date>2012-12-20T23:25:35+00:00</dc:date>
    <link>http://www.exp-platform.com/Pages/PuzzingOutcomesExplained.aspx</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Online controlled experiments are often utilized to make data-driven decisions at Amazon, Microsoft, eBay, Facebook, Google, Yahoo, Zynga, and at many other companies.  While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale—thousands of experiments now—has taught us many lessons.  These exemplify the proverb that the difference between theory and practice is greater in practice than in theory. We present our learnings as they happened: puzzling outcomes of controlled experiments that we analyzed deeply to understand and explain.  Each of these took multiple-person weeks to months to properly analyze and get to the often surprising root cause. The root causes behind these puzzling results are not isolated incidents; these issues generalized to multiple experiments. The heightened awareness should help readers increase the trustworthiness of the results coming out of controlled experiments.   At Microsoft’s Bing, it is not uncommon to see experiments that impact annual revenue by millions of dollars, thus getting trustworthy results is critical and investing in understanding anomalies has tremendous payoff: reversing a single incorrect decision based on the results of an experiment can fund a whole team of analysts.   The topics we cover include: the OEC (Overall Evaluation Criterion), click tracking, effect trends, experiment length and power, and carryover effects."]]></description>
<dc:subject>via:arsyed experimental_design user_interfaces statistics have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bf346eee551b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:user_interfaces"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nytimes.com/2011/11/27/opinion/sunday/willpower-its-in-your-head.html">
    <title>Willpower - It’s in Your Head - NYTimes.com</title>
    <dc:date>2012-10-20T12:37:08+00:00</dc:date>
    <link>http://www.nytimes.com/2011/11/27/opinion/sunday/willpower-its-in-your-head.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[But I _like_ the glucose story!]]></description>
<dc:subject>moral_psychology track_down_references dweck.carol experimental_psychology via:arsyed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c1ffc97e39a0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moral_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dweck.carol"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nicebread.de/visually-weighted-regression-in-r-a-la-solomon-hsiang/">
    <title>Visually weighted regression in R (à la Solomon Hsiang)</title>
    <dc:date>2012-08-30T14:38:52+00:00</dc:date>
    <link>http://www.nicebread.de/visually-weighted-regression-in-r-a-la-solomon-hsiang/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>regression statistics R visual_display_of_quantitative_information to_teach:undergrad-ADA via:arsyed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2a202078fa0e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://httpcats.herokuapp.com/">
    <title>HTTP Status Cats</title>
    <dc:date>2011-12-16T01:28:36+00:00</dc:date>
    <link>http://httpcats.herokuapp.com/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Usage: http://httpcats.herokuapp.com/[http_status_code]"]]></description>
<dc:subject>funny:geeky lolcats web via:arsyed via:arthegall to:blog</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0465674a2dc4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:funny:geeky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lolcats"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arthegall"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.lrb.co.uk/v33/n21/pankaj-mishra/watch-this-man">
    <title>Pankaj Mishra reviews ‘Civilisation’ by Niall Ferguson (LRB 3 November 2011)</title>
    <dc:date>2011-11-11T20:29:29+00:00</dc:date>
    <link>http://www.lrb.co.uk/v33/n21/pankaj-mishra/watch-this-man</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>book_reviews ferguson.niall imperialism world_history via:arsyed mishra.pankaj racist_idiocy gives_historians_a_bad_name utter_stupidity the_decline_of_the_west running_dogs_of_reaction evisceration</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:46b3cb2ec013/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ferguson.niall"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:imperialism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:world_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mishra.pankaj"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racist_idiocy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gives_historians_a_bad_name"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:utter_stupidity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_decline_of_the_west"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:running_dogs_of_reaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evisceration"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.pinboard.in/2011/10/the_fans_are_all_right/">
    <title>The Fans Are All Right (Pinboard Blog)</title>
    <dc:date>2011-10-07T02:07:40+00:00</dc:date>
    <link>http://blog.pinboard.in/2011/10/the_fans_are_all_right/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I learned a lot about fandom couple of years ago in conversations with my friend Britta, who was working at the time as community manager for Delicious. She taught me that fans were among the heaviest users of the bookmarking site, and had constructed an edifice of incredibly elaborate tagging conventions, plugins, and scripts to organize their output along a bewildering number of dimensions. If you wanted to read a 3000 word fic where Picard forces Gandalf into sexual bondage, and it seems unconsensual but secretly both want it, and it's R-explicit but not NC-17 explicit, all you had to do was search along the appropriate combination of tags (and if you couldn't find it, someone would probably write it for you). By 2008 a whole suite of theoretical ideas about folksonomy, crowdsourcing, faceted infomation retrieval, collaborative editing and emergent ontology had been implemented by a bunch of friendly people so that they could read about Kirk drilling Spock."  --- See also the very last link.]]></description>
<dc:subject>fandom social_life_of_the_mind social_media information_retrieval tagging pinboard via:arsyed to_teach:data-mining ok_maybe_not_really_to_teach delicious.com</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aa9a7bc950f1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fandom"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tagging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pinboard"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ok_maybe_not_really_to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:delicious.com"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.bioscienceresource.org/commentaries/article.php?id=46">
    <title>The Great DNA Data Deficit: Are Genes for Disease a Mirage? (Jonathan Latham and Allison Wilson)</title>
    <dc:date>2010-12-13T23:40:47+00:00</dc:date>
    <link>http://www.bioscienceresource.org/commentaries/article.php?id=46</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[ETA: I withdraw my approval, and question my own reading skills.  See
http://bayes.wordpress.com/2010/12/14/it-takes-three-to-read-this-stupid-article-marge-two-to-write-it-and-one-to-read-it/
]]></description>
<dc:subject>genetics heritability human_genetics genomics re:g_paper via:arsyed link_left_as_a_reminder_to_self</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:619959d8532f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heritability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:human_genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:link_left_as_a_reminder_to_self"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://books.google.com/books?id=ziDGGIkhqlMC">
    <title>Statistical inference based on divergence measures (Leandro Pardo Llorente)</title>
    <dc:date>2010-11-07T13:26:40+00:00</dc:date>
    <link>http://books.google.com/books?id=ziDGGIkhqlMC</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:noted information_theory statistics via:arsyed</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee580df041dd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.johnmyleswhite.com/notebook/2010/08/17/unit-testing-in-r-the-bare-minimum/">
    <title>Unit Testing in R: The Bare Minimum</title>
    <dc:date>2010-08-20T12:09:15+00:00</dc:date>
    <link>http://www.johnmyleswhite.com/notebook/2010/08/17/unit-testing-in-r-the-bare-minimum/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I hesitate about the teaching tag, this seems quite clunky --- but perhaps it's not that bad when you try it.
]]></description>
<dc:subject>via:arsyed programming R to_teach:data-mining to_teach:statcomp</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3dadc605404d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://micromath.wordpress.com/2008/04/14/donald-knuth-calculus-via-o-notation/">
    <title>Donald Knuth: Calculus via O notation « Mathematics under the Microscope</title>
    <dc:date>2010-08-07T12:31:51+00:00</dc:date>
    <link>http://micromath.wordpress.com/2008/04/14/donald-knuth-calculus-via-o-notation/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Lovely.
]]></description>
<dc:subject>calculus teaching via:arsyed knuth.donald</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4504ec4825f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:calculus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:knuth.donald"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://en.wikipedia.org/wiki/Phantom_of_Heilbronn">
    <title>Phantom of Heilbronn - Wikipedia, the free encyclopedia</title>
    <dc:date>2010-05-30T14:01:59+00:00</dc:date>
    <link>http://en.wikipedia.org/wiki/Phantom_of_Heilbronn</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[In which the combined police forces of Europe spend years chasing a female serial killer known only from DNA evidence, only to find that it's all down to contaminated cotton swabs from a single supplier!

Teaching note for data mining: This should make a great example of the importance of getting the data right, before worrying about the statistical processing...
]]></description>
<dc:subject>via:arsyed serial_killers to_teach:data-mining bad_data DNA_testing forensics wtf inference_to_latent_objects blogged</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e7dbba5ca908/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:serial_killers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:DNA_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:forensics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wtf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogged"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.globalpost.com/dispatch/pakistan/100219/taliban-pakistan-baluchistan?page=0,0">
    <title>The Goddess of Taliban Country</title>
    <dc:date>2010-05-07T16:11:14+00:00</dc:date>
    <link>http://www.globalpost.com/dispatch/pakistan/100219/taliban-pakistan-baluchistan?page=0,0</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Pretty travel writing from Baluchistan.
]]></description>
<dc:subject>baluchistan pakistan travelers'_tales via:arsyed</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4ffda02a0cab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:baluchistan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pakistan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:travelers'_tales"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.emis.de/journals/JEHPS/juin2009.html">
    <title>Splendors and Miseries of Martingales</title>
    <dc:date>2010-04-24T16:23:24+00:00</dc:date>
    <link>http://www.emis.de/journals/JEHPS/juin2009.html</link>
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