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    <description>recent bookmarks from dvse</description>
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	<rdf:li rdf:resource="http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19860003843_1986003843.pdf"/>
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  </channel><item rdf:about="http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/publications/StatisticalLearningTheory.pdf">
    <title>Statistical Learning Theory: Models, Concepts, and Results</title>
    <dc:date>2013-05-10T04:06:01+00:00</dc:date>
    <link>http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/publications/StatisticalLearningTheory.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Statistical learning theory provides the theoretical basis for many of today’s machine learning algorithms and is arguably one of the most beautifully developed branches of artificial intelligence in general. It originated in Russia in the 1960s and gained wide popularity in the 1990s following the development of the so-called Support Vector Machine (SVM), which has become a standard tool for pattern recognition in a variety of domains ranging from computer vision to computational biology. Providing the basis of new learning algorithms, however, was not the only motivation for developing statistical learning theory. It was just as much a philosophical one, attempting to answer the question of what it is that allows us to draw valid conclusions from empirical data."]]></description>
<dc:subject>statistics machine_learning statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:f9d0b888c39d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistical_learning_theory"/>
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</item>
<item rdf:about="http://homes.di.unimi.it/~cesabian/Pubblicazioni/banditSurvey.pdf">
    <title>Regret analysis of stochastic and nonstochastic multi-armed bandit problems</title>
    <dc:date>2013-04-06T13:05:43+00:00</dc:date>
    <link>http://homes.di.unimi.it/~cesabian/Pubblicazioni/banditSurvey.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Multi-armed bandit problems are the most basic examples of sequential decision problems with an exploration–exploitation trade-off. This is the balance between staying with the option that gave highest payoffs in the past and exploring new options that might give higher payoffs in the future. Although the study of bandit problems dates back to the 1930s, exploration–exploitation trade-offs arise in several modern applications, such as ad placement, website optimization, and packet routing."]]></description>
<dc:subject>statistics control_theory bandits survey</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:114e04b7a708/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:control_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:bandits"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:survey"/>
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<item rdf:about="http://www.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf">
    <title>Strictly Proper Scoring Rules, Prediction, and Estimation</title>
    <dc:date>2013-02-16T10:09:07+00:00</dc:date>
    <link>http://www.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if he or she issues the probabilistic forecast F, rather than G = F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful assessments and to be honest."]]></description>
<dc:subject>statistics decision_theory estimation economics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:d544339a4ed7/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:decision_theory"/>
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<item rdf:about="http://arxiv.org/pdf/1301.3578v2.pdf">
    <title>Cramer-Rao Lower Bound and Information Geometry</title>
    <dc:date>2013-02-15T07:32:49+00:00</dc:date>
    <link>http://arxiv.org/pdf/1301.3578v2.pdf</link>
    <dc:creator>dvse</dc:creator><dc:subject>statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:477e3f35fc59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
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<item rdf:about="http://cowles.econ.yale.edu/~hes/pub/studies-12.pdf">
    <title>A min-max solution to an inventory problem</title>
    <dc:date>2012-12-30T11:50:30+00:00</dc:date>
    <link>http://cowles.econ.yale.edu/~hes/pub/studies-12.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[An early (1958) result for the distribution-free risk-neutral newsvendor problem. Identical to "no-arbitrage bounds". ]]></description>
<dc:subject>finance convex_optimization operations_research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:60ef0ad7a5cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:finance"/>
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<item rdf:about="http://arxiv.org/pdf/quant-ph/0101012v4.pdf">
    <title>Quantum Theory from Five Reasonable Axioms</title>
    <dc:date>2012-12-29T14:57:26+00:00</dc:date>
    <link>http://arxiv.org/pdf/quant-ph/0101012v4.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[An elementary introduction to quantum mechanics as a modification of a discrete state space hidden markov model.]]></description>
<dc:subject>quantum_mechanics probability graphical_models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:37cfa6ead8aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:quantum_mechanics"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:graphical_models"/>
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<item rdf:about="http://arxiv.org/pdf/0708.0948.pdf">
    <title>Pricing, hedging and optimally designing derivatives via minimization of risk measures</title>
    <dc:date>2012-12-28T12:18:55+00:00</dc:date>
    <link>http://arxiv.org/pdf/0708.0948.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["The question of pricing and hedging a given contingent claim has a unique solution in a complete market framework. When some incompleteness is introduced, the problem becomes however more difficult. Several approaches have been adopted in the ..."

A specimen of OCE reinvention in the "mathematical finance" literature.]]></description>
<dc:subject>convex_optimization risk_measures online_learning finance</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:6534fd41c413/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:online_learning"/>
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<item rdf:about="http://emotion.technion.ac.il/Labs/Opt/opt/Pap/oce-risk.pdf">
    <title>An old-new concept of convex risk measures: the optimized certainty equivalent</title>
    <dc:date>2012-10-27T14:59:03+00:00</dc:date>
    <link>http://emotion.technion.ac.il/Labs/Opt/opt/Pap/oce-risk.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["The optimized certainty equivalent (OCE) is a decision theoretic criterion based on a utility function, that was first introduced by the authors in 1986. This paper re-examines this fundamental concept, studies and extends its main properties, and puts it in perspective to recent concepts of risk measures. We show that the negative of the OCE naturally provides a wide family of risk measures that fits the axiomatic formalism of convex risk measures. Duality theory is used to reveal the link between the OCE and the ϕ-divergence functional (a generalization of relative entropy), and allows for deriving various variational formulas for risk measures. Within this interpretation of the OCE, we prove that several risk measures recently analyzed and proposed in the literature (e.g., conditional value of risk, bounded shortfall risk) can be derived as special cases of the OCE by using particular utility functions. We further study the relations between the OCE and other certainty equivalents, providing general conditions under which these can be viewed as coherent/convex risk measures. Throughout the paper several examples illustrate the flexibility and adequacy of the OCE for building risk measures."]]></description>
<dc:subject>convex_optimization decision_theory finance risk_measures online_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:ea8f777bd125/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:finance"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:online_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.statslab.cam.ac.uk/~rrw1/stats/Sa4.pdf">
    <title>Cambridge Statistics IB</title>
    <dc:date>2012-10-16T08:13:40+00:00</dc:date>
    <link>http://www.statslab.cam.ac.uk/~rrw1/stats/Sa4.pdf</link>
    <dc:creator>dvse</dc:creator><dc:subject>statistics hypothesis_testing generalized_likelihood_ratio</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:9a361b77d672/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:generalized_likelihood_ratio"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.csd.uoc.gr/~komod/ICCV2011_tutorial/">
    <title>Learning with Inference for Discrete Graphical Models (ICCV 2011 tutorial)</title>
    <dc:date>2012-10-04T04:22:19+00:00</dc:date>
    <link>http://www.csd.uoc.gr/~komod/ICCV2011_tutorial/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Several problems in computer vision, pattern recognition, medical imaging and signal processing can be formulated using the discrete graphical models framework. The two main issues faced by researchers when using graphical models are: (i) Learning: How to estimate the parameters of the model?; and (ii) Inference: How to find the best assignment for the variables of the model? In this tutorial we will discuss these two issues, starting from the basics and building up to the state of the art. "]]></description>
<dc:subject>convex_optimization exponential_family machine_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:897c8affda90/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:exponential_family"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
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</item>
<item rdf:about="http://arxiv.org/abs/math-ph/0002049">
    <title>Classical and Quantum Probability</title>
    <dc:date>2012-08-11T15:25:00+00:00</dc:date>
    <link>http://arxiv.org/abs/math-ph/0002049</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["We survey the development of probability from 1900, starting with Bachelier's theory of speculation. Fisher information appears in the theory of estimation. We touch on Brownian motion, and the Wiener integral. The Ito calculus, and its relation to to the heat equation, is mentioned. Quantum theory is introduced as a generalisation of probability, rather than of mechanics. The weakness of attempts to describe quantum theory in terms of hidden variables is explained, by a simple proof of Bell's inequality.
Quantum versions of the Langevin equation are discussed, and the theory of continuous tensor products is used to give a possible quantum version. The quantum stochastic calculus of Barnett, Wilde and the author, as well as that of Parthasarathy and Hudson, is introduced."]]></description>
<dc:subject>statistics foundations finance quantum_mechanics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:1ba1cc2416ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:quantum_mechanics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf">
    <title>Causal Inference in Statistics: An Overview</title>
    <dc:date>2012-08-11T14:32:48+00:00</dc:date>
    <link>http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals."]]></description>
<dc:subject>causal_inference statistics econometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:241872b3b584/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:econometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.imsc/1207580081">
    <title>On the history and use of some standard statistical models</title>
    <dc:date>2012-07-30T04:52:24+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.imsc/1207580081</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This paper tries to tell the story of the general linear model, which saw the light of day 200 years ago, and the assumptions underlying it. We distinguish three principal stages (ignoring earlier more isolated instances). The model was first proposed in the context of astronomical and geodesic observations, where the main source of variation was observational error. This was the main use of the model during the 19th century.

In the 1920’s it was developed in a new direction by R.A. Fisher whose principal applications were in agriculture and biology. Finally, beginning in the 1930’s and 40’s it became an important tool for the social sciences. As new areas of applications were added, the assumptions underlying the model tended to become more questionable, and the resulting statistical techniques more prone to misuse."]]></description>
<dc:subject>statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:d46068115267/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.eecs.harvard.edu/econcs/pubs/Bacon_aamas12.pdf">
    <title>Predicting Your Own Effort</title>
    <dc:date>2012-07-29T09:04:05+00:00</dc:date>
    <link>http://www.eecs.harvard.edu/econcs/pubs/Bacon_aamas12.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[We consider a setting in which a worker and a manager may each have information about the likely completion time of a task, and the worker also aects the completion time by choosing a level of eort. The task itself may further be composed of a set of subtasks, and the worker can also decide how many of these subtasks to split out into an explicit prediction task. In addition, the worker can learn about the likely completion time of a task as work on subtasks completes. We characterize a family of scoring rules for the worker and manager that provide three properties: information is truthfully reported; best eort is exerted by the worker in completing tasks as quickly as possible; and collusion is not possible. We also study the factors influencing when a worker will split a task into subtasks, each forming a separate prediction target"]]></description>
<dc:subject>mechanism_design economics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:d75d44b4ea86/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:mechanism_design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://people.seas.harvard.edu/~jklai/pubs/paymentrules-ec12.pdf">
    <title>Payment Rules through Discriminant-Based Classiﬁers</title>
    <dc:date>2012-07-29T09:01:35+00:00</dc:date>
    <link>http://people.seas.harvard.edu/~jklai/pubs/paymentrules-ec12.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex-post regret, we are able to adapt techniques of statistical machine learning to the design of payment rules. This computational approach to mechanism design is applicable to domains with multidimensional types and situations where computational efﬁciency is a concern. Speciﬁcally, given an outcome rule and access to a type distribution, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex-post regret, and that penalizing classiﬁcation errors is effective in preventing failures of ex-post individual rationality"]]></description>
<dc:subject>machine_learning mechanism_design economics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:c4c47928db86/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:mechanism_design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.psych.umn.edu/faculty/waller/classes/mult12/readings/sweep1979.pdf">
    <title>A Tutorial on the SWEEP Operator</title>
    <dc:date>2012-07-25T14:11:04+00:00</dc:date>
    <link>https://www.psych.umn.edu/faculty/waller/classes/mult12/readings/sweep1979.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["First, this tutorial describes the use of Gauss-Jordan elimination for least squares and continues with a description of a completely generalized sweep operator ..."]]></description>
<dc:subject>linear_algebra statistics numerical_methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:01e47d1d83cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:linear_algebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:numerical_methods"/>
</rdf:Bag></taxo:topics>
</item>
<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://www.casact.org/pubs/proceed/proceed99/99317.pdf">
    <title>A Systematic Relationship between Minimum Bias and Generalized Linear Models</title>
    <dc:date>2012-07-12T05:35:22+00:00</dc:date>
    <link>http://www.casact.org/pubs/proceed/proceed99/99317.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Those were the days when insurance was one of the most complex applications of statistics around.]]></description>
<dc:subject>actuarial statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:130311a6a41c/</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:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/index.htm">
    <title>MIT 6.262 - Discrete Stochastic Processes</title>
    <dc:date>2012-07-04T07:59:20+00:00</dc:date>
    <link>http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/index.htm</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Course by Robert Gallager]]></description>
<dc:subject>statistics stochastic_processes video_lectures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:1fed794ec188/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:video_lectures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.admb-project.org/">
    <title>ADMB Project</title>
    <dc:date>2012-07-02T05:29:02+00:00</dc:date>
    <link>http://www.admb-project.org/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["AD Model Builder, or ADMB, the most powerful software package for the development of state-of-the-art nonlinear models, can now be freely downloaded for Windows, Linux, MacOS, and Sun/SPARC "]]></description>
<dc:subject>optimization statistics random_effects software</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:5ff9ce6f439a/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:random_effects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:software"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.seas.harvard.edu/courses/cs281/">
    <title>CS281: Advanced Machine Learning (Harvard)</title>
    <dc:date>2012-07-01T09:34:26+00:00</dc:date>
    <link>http://www.seas.harvard.edu/courses/cs281/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Many links to video lectures.]]></description>
<dc:subject>machine_learning statistics video_lectures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:015b6413d563/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:video_lectures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mth.kcl.ac.uk/~teemu/cof.pdf">
    <title>Introduction to convex optimization in financial markets</title>
    <dc:date>2012-06-30T12:01:39+00:00</dc:date>
    <link>http://www.mth.kcl.ac.uk/~teemu/cof.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Intro by Teemu Pennainen. Much of "mathematical finance" is best viewed as a collection of optimization problems (duality between "probabilities" and portfolio weights). ]]></description>
<dc:subject>convex_optimization actuarial finance economics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:7573c786e138/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:actuarial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
</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://www.mat.unimi.it/users/frittelli/pdf/CertaintyEquivalent1997.pdf">
    <title>Certainty Equivalent and No Arbitrage</title>
    <dc:date>2012-06-18T05:24:03+00:00</dc:date>
    <link>http://www.mat.unimi.it/users/frittelli/pdf/CertaintyEquivalent1997.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Maximum entropy measure vs. portfolio optimisation with exponential utility]]></description>
<dc:subject>convex_optimization economics exponential_family</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:e19635ad01e2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:exponential_family"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ieor.berkeley.edu/~lim/downloads/robustpricingfinal.pdf">
    <title>Relative Entropy, Exponential Utility, and Robust Dynamic Pricing</title>
    <dc:date>2012-06-14T07:08:23+00:00</dc:date>
    <link>http://www.ieor.berkeley.edu/~lim/downloads/robustpricingfinal.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["In the area of dynamic revenue management, optimal pricing policies are typically computed on the basis of an underlying demand rate model. From the perspective of applications, this approach implicitly assumes that the model is an accurate representation of the real-world demand process and that the parameters characterizing this model can be accurately calibrated using data. In many situations, neither of these conditions are satisfied. Indeed, models are usually simplified for the purpose of tractability and may be difficult to calibrate because of a lack of data. Moreover, pricing policies that are computed under the assumption that the model is correct may perform badly when this is not the case. This paper presents an approach to single-product dynamic revenue management that accounts for errors in the underlying model at the optimization stage."]]></description>
<dc:subject>stochastic_optimization pricing operations_research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:955e2109a39e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:pricing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:operations_research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://http://www.ece.ust.hk/~palomar/ELEC5470_lectures/25/Mung_nonconvex.pdf">
    <title>Nonconvex Optimization for Communication Systems</title>
    <dc:date>2012-06-07T02:12:15+00:00</dc:date>
    <link>http://http://www.ece.ust.hk/~palomar/ELEC5470_lectures/25/Mung_nonconvex.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Convex optimization has provided both a powerful tool and an intriguing mentality to the analysis and design of communication systems over the last few years. A main challenge today is on nonconvex problems in these application. This paper presents an overview of some of the important nonconvex optimization problems in point-to-point and networked communication systems. Three typical applications are covered: Internet congestion control through nonconcave network utility maximization, wireless network power control through geometric and sigmoidal programming, and DSL spectrum management through distributed nonconvex optimization."]]></description>
<dc:subject>convex_optimization distributed_optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:d043ce167755/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:distributed_optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.mcgill.ca/~vkules/bandits.pdf">
    <title>Algorithms for the multi-armed bandit problems</title>
    <dc:date>2012-05-30T00:26:48+00:00</dc:date>
    <link>http://www.cs.mcgill.ca/~vkules/bandits.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["The stochastic multi-armed bandit problem is an important model for studying the exploration-exploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical conrmation of their eectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms."]]></description>
<dc:subject>stochastic_control statistics bandits</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:72710c8205cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:bandits"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf">
    <title>Statistical learning theory and sequential prediction</title>
    <dc:date>2012-05-27T07:20:02+00:00</dc:date>
    <link>http://stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[UPenn STAT928 notes]]></description>
<dc:subject>statistics convex_optimization asymptotics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:b6f653dda075/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:asymptotics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.dme.im.ufrj.br/arquivos/publicacoes/arquivo249.pdf">
    <title>Generalized Linear Models with Random Eﬀects in the Two-Parameter Exponential Family</title>
    <dc:date>2012-05-11T12:01:00+00:00</dc:date>
    <link>http://www.dme.im.ufrj.br/arquivos/publicacoes/arquivo249.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["In this paper we develop a new class of double generalized linear models, introducing a random eﬀect component in the link function describing the linear predictor related to the precision parameter. This is a useful procedure to take into account extra variability and also to make the model more robust. The Bayesian paradigm is adopted to make inference in this class of models. Samples of the joint posterior distribution are draw using standard MCMC procedures. Finally, we illustrate this algorithm by considering simulated and real data sets."]]></description>
<dc:subject>exponential_family statistics convexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:0fb60fa48605/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:exponential_family"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convexity"/>
</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://statistics.stanford.edu/~ckirby/techreports/ONR/SOL%20ONR%20481.pdf">
    <title>Overdispersed generalized linear models</title>
    <dc:date>2012-05-10T03:44:23+00:00</dc:date>
    <link>http://statistics.stanford.edu/~ckirby/techreports/ONR/SOL%20ONR%20481.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Generalized linear models have become a. standard class of models for data analysts. However in some applications, heterogeneity in samples is too great to be explained by the simple variance function implicit in such models. Utilizing a. two parameter exponential family which is overdispersed relative to a specified one parameter exponential family enables the  creation of classes of overdispersed generalized linear models (OGLM’s) which are analytically attractive."]]></description>
<dc:subject>statistics exponential_family</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:4a935b3835e6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:exponential_family"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://economics.mit.edu/files/5810">
    <title>Selection in Insurance Markets: Theory and Empirics in Pictures</title>
    <dc:date>2012-05-09T14:32:45+00:00</dc:date>
    <link>http://economics.mit.edu/files/5810</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Over the last decade, however, empirical work on selection in insurance markets has gained considerable momentum, and a fairly extensive (and still growing) empirical literature on the topic has emerged. This research has found that adverse selection exists in some insurance markets but not in others. It has also uncovered examples of markets that exhibit “advantageous selection”—a phenomenon not considered by the original theory, and one that has different consequences for equilibrium insurance allocation and optimal public policy than the classical case of adverse selection. Researchers have also taken steps toward estimating the welfare consequences of detected selection and of potential public policy interventions."]]></description>
<dc:subject>actuarial economics adverse_selection</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:0b2674772fb3/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:adverse_selection"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pages.cs.wisc.edu/~ferris/papers/sirev-comp-app.pdf">
    <title>Engineering and economics applications of complementarity problems</title>
    <dc:date>2012-05-03T14:49:24+00:00</dc:date>
    <link>http://pages.cs.wisc.edu/~ferris/papers/sirev-comp-app.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This paper gives an extensive documentation of applications of ﬁnite-dimensional nonlinear complementarity problems in engineering and equilibrium modeling. For most applications, we describe the problem brieﬂy, state the deﬁning equations of the model, and give functional
expressions for the complementarity formulations. The goal of this documentation is threefold: (i) to summarize the essential applications of the nonlinear complementarity problem known to date, (ii) to provide a basis for the continued research on the nonlinear complementarity problem, and (iii) to supply a broad collection of realistic complementarity problems for use in algorithmic experimentation and other studies.]]></description>
<dc:subject>optimization general_equilibrium convex_optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:19512492ce31/</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:general_equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cowles.econ.yale.edu/P/cd/d12b/d1272.pdf">
    <title>How to compute equilibrium prices in 1891</title>
    <dc:date>2012-04-27T05:24:19+00:00</dc:date>
    <link>http://cowles.econ.yale.edu/P/cd/d12b/d1272.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[The original equilibrium model of Irving Fisher]]></description>
<dc:subject>economics general_equilibrium</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:56e76d6015be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:general_equilibrium"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.google.com.au/url?sa=t&amp;rct=j&amp;q=convex%20equilibrium%20ye&amp;source=web&amp;cd=1&amp;ved=0CCMQFjAA&amp;url=http%3A%2F%2Fwww.stanford.edu%2F~yyye%2Fconvexpricingms.pdf&amp;ei=2CmaT9SxK5CRiQfwxsThDg&amp;usg=AFQjCNHalz9BtIsunNP0XYAxB5Ct0yHrcg">
    <title>On Equilibrium Pricing as Convex Optimization</title>
    <dc:date>2012-04-27T05:09:52+00:00</dc:date>
    <link>http://www.google.com.au/url?sa=t&amp;rct=j&amp;q=convex%20equilibrium%20ye&amp;source=web&amp;cd=1&amp;ved=0CCMQFjAA&amp;url=http%3A%2F%2Fwww.stanford.edu%2F~yyye%2Fconvexpricingms.pdf&amp;ei=2CmaT9SxK5CRiQfwxsThDg&amp;usg=AFQjCNHalz9BtIsunNP0XYAxB5Ct0yHrcg</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["We study competitive economy equilibrium computation. We show that, for the first time, the equilibrium sets of the following two markets: 1. A mixed Fisher and Arrow-Debreu market with homogeneous and log-concave utility func-tions; 2. The Fisher and Arrow-Debreu markets with several classes of concave non-homogeneous utility functions; are convex or log-convex. Furthermore, an equilibrium can be computed as convex optimization by an interior-point algorithm in polynomial time."

NB: The difference between Fisher and Arrow-Debreu equilibria]]></description>
<dc:subject>convex_optimization economics general_equilibrium</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:5877a58d0583/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:general_equilibrium"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.st-andrews.ac.uk/cdma/seminars/Altug%20Paper.pdf">
    <title>Complete and Incomplete Market Models</title>
    <dc:date>2012-04-23T10:25:39+00:00</dc:date>
    <link>http://www.st-andrews.ac.uk/cdma/seminars/Altug%20Paper.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["In competitive asset markets, consumers make intertemporal choices in an uncertain environment. Their attitudes toward risk, production opportunities, and the nature of trades that they can enter into determine equilibrium quantities and the prices of assets that are traded. The intertemporal choice problem of a consumer in an uncertain environment yields restrictions for the behavior of individual consumption over time as well as determining the form of the asset pricing function used to price random payoffs."]]></description>
<dc:subject>convex_optimization economics general_equilibrium</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:960f8ecd611f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:general_equilibrium"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf">
    <title>Learning Deep Architectures for AI</title>
    <dc:date>2012-04-17T04:30:46+00:00</dc:date>
    <link>http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks."]]></description>
<dc:subject>machine_learning neural_networks optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:fb0f4d0f889a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.informationgeometry.org/resources/NoteLegendreTransformation.pdf">
    <title>Legendre transformation and information geometry</title>
    <dc:date>2012-04-12T06:08:01+00:00</dc:date>
    <link>http://blog.informationgeometry.org/resources/NoteLegendreTransformation.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Legendre transformation is at the heart of the duality principle of ﬂat information geometries. Let us explain intuitively this transformation using geometric reasoning. (We shall
skip proofs and concentrate on the essence of the transformation instead."]]></description>
<dc:subject>convex_optimization information_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:8e425d08fa50/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:information_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1111.2664">
    <title>A Collaborative Mechanism for Crowdsourcing Prediction Problems</title>
    <dc:date>2012-04-09T12:37:43+00:00</dc:date>
    <link>http://arxiv.org/abs/1111.2664</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of "crowdsourcing" prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively "learn" a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and participants can modify this hypothesis by wagering on an update. The critical incentive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set. "]]></description>
<dc:subject>mechanism_design convex_optimization game_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:d4e8cdba119e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:mechanism_design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:game_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rsta.royalsocietypublishing.org/content/222/594-604/309.full.pdf">
    <title>On the mathematical foundations of theoretical statistics</title>
    <dc:date>2012-04-01T13:07:29+00:00</dc:date>
    <link>http://rsta.royalsocietypublishing.org/content/222/594-604/309.full.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[R.A Fisher's classical paper outlining the maximum likelihood principle and the notions of sufficiency, efficiency and consistency.]]></description>
<dc:subject>statistics foundations estimation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:4834fa210d16/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:estimation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://economics.mit.edu/files/5326">
    <title>The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics</title>
    <dc:date>2012-04-01T13:01:37+00:00</dc:date>
    <link>http://economics.mit.edu/files/5326</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Since Edward Leamer's memorable 1983 paper, "Let's Take the Con out of Econometrics," empirical microeconomics has experienced a credibility revolution. While Leamer's suggested remedy, sensitivity analysis, has played a role in this, we argue that the primary engine driving improvement has been a focus on the quality of empirical research designs. The advantages of a good research design are perhaps most easily apparent in research using random assignment. We begin with an overview of Leamer's 1983 critique and his proposed remedies. We then turn to the key factors we see contributing to improved empirical work, including the availability of more and better data, along with advances in theoretical econometric understanding, but especially the fact that research design has moved front and center in much of empirical micro. We offer a brief digression into macroeconomics and industrial organization, where progress -- by our lights -- is less dramatic, although there is work in both fields that we find encouraging. Finally, we discuss the view that the design pendulum has swung too far. Critics of design-driven studies argue that in pursuit of clean and credible research designs, researchers seek good answers instead of good questions. We briefly respond to this concern, which worries us little."

IV / natural experiment proponents revisiting Leamer's critique.]]></description>
<dc:subject>econometrics foundations estimation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:b1a5ae558231/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:estimation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nowpublishers.com/product.aspx?product=MAL&amp;doi=2200000018">
    <title>Online Learning and Online Convex Optimization</title>
    <dc:date>2012-04-01T12:57:12+00:00</dc:date>
    <link>http://www.nowpublishers.com/product.aspx?product=MAL&amp;doi=2200000018</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Online learning is a well established learning paradigm which has both theoretical and practical appeals. The goal of online learning is to make a sequence of accurate predictions given knowledge of the correct answer to previous prediction tasks and possibly additional available information. Online learning has been studied in several research fields including game theory, information theory, and machine learning. It also became of great interest to practitioners due the recent emergence of large scale applications such as online advertisement placement and online web ranking. In this survey we provide a modern overview of online learning. Our goal is to give the reader a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms. We do not mean to be comprehensive but rather to give a high-level, rigorous yet easy to follow, survey."

http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf]]></description>
<dc:subject>convex_optimization machine_learning</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:bf8862bdb24e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:convex_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://courses.csail.mit.edu/6.851/spring12/">
    <title>MIT 6.851: Advanced Data Structures</title>
    <dc:date>2012-02-15T01:23:20+00:00</dc:date>
    <link>http://courses.csail.mit.edu/6.851/spring12/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structures"]]></description>
<dc:subject>courses video_lectures algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:7a8ba28844f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:courses"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:video_lectures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.umn.edu/geyer/lecam/">
    <title>Le Cam Made Simple: No-N Asymptotics</title>
    <dc:date>2012-02-15T00:48:44+00:00</dc:date>
    <link>http://www.stat.umn.edu/geyer/lecam/</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["If the log likelihood is approximately quadratic with constant Hessian, then the maximum likelihood estimator (MLE) is approximately normally distributed. No other assumptions are required. We do not need independent and identically distributed data. We do not need the law of large numbers (LLN) or the central limit theorem (CLT). We do not need sample size going to infinity or anything going to infinity.

The theory presented here is a combination of Le Cam style involving local asymptotic normality (LAN) and local asymptotic mixed normality (LAMN) and Cramér style involving derivatives and Fisher information. The main tool is convergence in law of the log likelihood function and its derivatives considered as random elements of a Polish space of continuous functions with the metric of uniform convergence on compact sets. We obtain results for both one-step-Newton estimators and Newton-iterated-to-convergence estimators."]]></description>
<dc:subject>statistics estimation asymptotics via:mraginsky</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:1c3bcdeeaf3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:asymptotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.international.ucla.edu/media/files/Leamer_article.pdf">
    <title>Let's take the Con out of Econometrics (Leamer)</title>
    <dc:date>2012-02-13T13:23:56+00:00</dc:date>
    <link>http://www.international.ucla.edu/media/files/Leamer_article.pdf</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[What to do when "experiments" can not be controlled (not much, but indoctrination helps) - predates current IV methodology?]]></description>
<dc:subject>econometrics foundations statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:ab99b9af5b7e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1112.0698">
    <title>[1112.0698] Machine Learning with Operational Costs</title>
    <dc:date>2012-02-13T12:37:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1112.0698</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Something not quite right about this - a distribution over the models would in principle capture all the relevant information for the planning subproblem - no need for joint optimization.]]></description>
<dc:subject>machine_learning decision_theory operations_research actuarial optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:bc98d52806de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:operations_research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:actuarial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.lnms/1249305323">
    <title>Huber: On the Non-Optimality of Optimal Procedures</title>
    <dc:date>2012-02-13T12:35:41+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.lnms/1249305323</link>
    <dc:creator>dvse</dc:creator><description><![CDATA["This paper discusses some subtle, and largely overlooked, differences between conceptual and mathematical optimization goals in statistics, and illustrates them by examples."
]]></description>
<dc:subject>statistics robust_statistics optimization foundations via:mraginsky</dc:subject>
<dc:identifier>https://pinboard.in/u:dvse/b:710548abe3f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:robust_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:foundations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.seas.upenn.edu/~jeromel/teaching/DP_fall09/DP.html">
    <title>Dynamic Programming and Stochastic Control</title>
    <dc:date>2012-02-13T12:29:07+00:00</dc:date>
    <link>http://www.seas.upenn.edu/~jeromel/teaching/DP_fall09/DP.html</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[Discrete time optimal control / approximate dynamic programming / LP formulation of the discrete time and space control problem]]></description>
<dc:subject>stochastic_control stochastic_optimization approximate_dp linear_programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:93a46e8d8ea7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:approximate_dp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:linear_programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.columbia.edu/~mh2078/DiscreteTimeFinance.html">
    <title>Financial Engineering: Discrete-Time Models (IEOR E4706)</title>
    <dc:date>2012-02-13T12:25:57+00:00</dc:date>
    <link>http://www.columbia.edu/~mh2078/DiscreteTimeFinance.html</link>
    <dc:creator>dvse</dc:creator><description><![CDATA[A concise introduction to discrete time "asset pricing". Doesn't make clear distinctions between marginal/equivalence pricing in "incomplete markets" or present "risk neutral probabilities" as dual variables]]></description>
<dc:subject>actuarial finance asset_pricing stochastic_optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:dvse/b:a506defc7e17/</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:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:asset_pricing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:dvse/t:stochastic_optimization"/>
</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"/>
</rdf:Bag></taxo:topics>
</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"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>