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    <title>Pinboard (dvse)</title>
    <link>https://pinboard.in/u:dvse/public/</link>
    <description>recent bookmarks from dvse</description>
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      <rdf:Seq>	<rdf:li rdf:resource="http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.4602"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1205.2265"/>
	<rdf:li rdf:resource="http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19860003843_1986003843.pdf"/>
	<rdf:li rdf:resource="http://www.jstor.org/stable/2281561"/>
	<rdf:li rdf:resource="http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;aid=3140568"/>
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  </channel><item rdf:about="http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf">
    <title>Regression and Causation: A Critical Examination of Econometrics Textbooks</title>
    <dc:date>2012-07-13T09:35:27+00:00</dc:date>
    <link>http://ftp.cs.ucla.edu/pub/stat_ser/r395.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This report surveys six influential econometric textbooks in terms of their math- ematical treatment of causal concepts. It highlights conceptual and notational differ- ences among the authors and points to areas where they deviate significantly from modern standards of causal analysis. We find that econonometric textbooks vary from complete denial to partial acceptance of the causal content of econometric equations and, uniformly, fail to provide coherent mathematical notation that distinguishes causal from statistical concepts. This survey also provides a panoramic view of the state of causal thinking in econometric education which, to the best of our knowledge, has not been surveyed before."
]]></description>
<dc:subject>causal_inference economics econometrics regression statistics via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:059611dcb7a1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.4602">
    <title>[1206.4602] Quasi-Newton Methods: A New Direction</title>
    <dc:date>2012-06-26T04:10:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.4602</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors."]]></description>
<dc:subject>optimization machine_learning via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:23302aee8b36/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1205.2265">
    <title>[1205.2265] Efficient Constrained Regret Minimization</title>
    <dc:date>2012-05-11T09:05:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1205.2265</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Online learning constitutes a mathematical framework to analyze sequential decision making problems in adversarial environments. The learner repeatedly chooses an action, the environment responds with an outcome, and then the learner receives a reward for the played action. The goal of the learner is to maximize his total reward. However, there are situations in which, in addition to maximizing the cumulative reward, there are some additional constraints/goals on the sequence of decisions that must be satisfied by the learner. For example, in textit{online marketing}, simultaneously maximizing the cumulative reward and the number of buyers to take advantage of word-of-mouth advertising for future marketing seems to be a more ambitious goal than only maximizing cumulative reward. As another example, learning from costly expert advice captures more realistic settings than the original setting in applications such as routing in networks with power constraint. In this paper we study an extension to the online learning where the learner aims to maximize the total reward given that some additional constraints need to be satisfied. We propose Lagrangian exponentially weighted average (textbf{LEWA}) algorithm, an efficient algorithm to solve constrained online learning, which is a primal dual variant of the well known exponentially weighted average algorithm and inspired by the theory of Lagrangian method in constrained optimization. We establish the regret and the violation of the constraint bounds in full information and bandit feedback models."]]></description>
<dc:subject>online_learning convex_optimization via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:72c22613b8d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:online_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19860003843_1986003843.pdf">
    <title>Discovery of the Kalman Filter as a Practical Tool for Aerospace and Industry</title>
    <dc:date>2012-02-13T11:32:23+00:00</dc:date>
    <link>http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19860003843_1986003843.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[History of the adoption of the Kalman filter in aero/astro
]]></description>
<dc:subject>control_theory state_estimation kalman_filter via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:e040e3b49f32/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:control_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:state_estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:kalman_filter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/stable/2281561">
    <title>A Method of Handling Curvilinear Correlation for Any Number of Variables (Ezekiel, 1924)</title>
    <dc:date>2012-02-13T11:08:43+00:00</dc:date>
    <link>http://www.jstor.org/stable/2281561</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Additive regression models from 1924, together with an algorithm which  looks even more labour intensive than Whittaker graduation!]]></description>
<dc:subject>regression additive_models statistics via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:fd58faecbfa1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:additive_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
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</item>
<item rdf:about="http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;aid=3140568">
    <title>On a New Method of Graduation</title>
    <dc:date>2012-02-13T11:06:57+00:00</dc:date>
    <link>http://journals.cambridge.org/action/displayAbstract?fromPage=online&amp;aid=3140568</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Whittaker introduces 1D smoothing in 1922, complete with the Bayesian derivation.   There is an earlier German paper with a similar model.]]></description>
<dc:subject>actuarial splines smoothing regression statistics via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:1c749ed847aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:actuarial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:splines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:smoothing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:cshalizi"/>
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