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  </channel><item rdf:about="http://proceedings.mlr.press/v75/golowich18a.html">
    <title>Size-Independent Sample Complexity of Neural Networks</title>
    <dc:date>2019-01-11T20:31:37+00:00</dc:date>
    <link>http://proceedings.mlr.press/v75/golowich18a.html</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[ We study the sample complexity of learning neural networks, by providing new bounds on their Rademacher complexity assuming norm constraints on the parameter matrix of each layer. Compared to previous work, these complexity bounds have improved dependence on the network depth, and under some additional assumptions, are fully independent of the network size (both depth and width). These results are derived using some novel techniques, which may be of independent interest. ]]></description>
<dc:subject>neural-net complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:65ed718b5824/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1805.04908">
    <title>[1805.04908] On the Practical Computational Power of Finite Precision RNNs for Language Recognition</title>
    <dc:date>2018-11-12T20:12:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1805.04908</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[ While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time. We consider the case of RNNs with finite precision whose computation time is linear in the input length. Under these limitations, we show that different RNN variants have different computational power. In particular, we show that the LSTM and the Elman-RNN with ReLU activation are strictly stronger than the RNN with a squashing activation and the GRU. This is achieved because LSTMs and ReLU-RNNs can easily implement counting behavior. We show empirically that the LSTM does indeed learn to effectively use the counting mechanism. ]]></description>
<dc:subject>nlp rnn complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:cf758803fcf0/</dc:identifier>
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<item rdf:about="https://rjlipton.wordpress.com/2010/11/07/what-is-a-complexity-class/">
    <title>What Is A Complexity Class? | Gödel's Lost Letter and P=NP</title>
    <dc:date>2018-06-14T21:54:18+00:00</dc:date>
    <link>https://rjlipton.wordpress.com/2010/11/07/what-is-a-complexity-class/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:06fdbe92d5fd/</dc:identifier>
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<item rdf:about="https://byorgey.wordpress.com/2018/02/28/test-your-intuition-logarithmic-time/">
    <title>Test your intuition: logarithmic time | blog :: Brent -&gt; [String]</title>
    <dc:date>2018-06-11T21:28:00+00:00</dc:date>
    <link>https://byorgey.wordpress.com/2018/02/28/test-your-intuition-logarithmic-time/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>problems complexity compsci intuition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:92323abe8a20/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1805.07883">
    <title>[1805.07883] How Many Samples are Needed to Learn a Convolutional Neural Network?</title>
    <dc:date>2018-05-29T16:40:26+00:00</dc:date>
    <link>https://arxiv.org/abs/1805.07883</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["A widespread folklore for explaining the success of convolutional neural network (CNN) is that CNN is a more compact representation than the fully connected neural network (FNN) and thus requires fewer samples for learning. We initiate the study of rigorously characterizing the sample complexity of learning convolutional neural networks. We show that for learning an m-dimensional convolutional filter with linear activation acting on a d-dimensional input, the sample complexity of achieving population prediction error of ϵ is O˜(m/ϵ2), whereas its FNN counterpart needs at least Ω(d/ϵ2) samples. Since m≪d, this result demonstrates the advantage of using CNN. We further consider the sample complexity of learning a one-hidden-layer CNN with linear activation where both the m-dimensional convolutional filter and the r-dimensional output weights are unknown. For this model, we show the sample complexity is O˜((m+r)/ϵ2) when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are localized empirical process and a new lemma characterizing the convolutional structure. We believe these tools may inspire further developments in understanding CNN."]]></description>
<dc:subject>neural-net convnet complexity learning-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:577820b76e6f/</dc:identifier>
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<item rdf:about="http://ericbalkanski.com/wp-content/uploads/2018/03/The-Adaptive-Complexity-of-Maximizing-a-Submodular-Function.pdf">
    <title>The Adaptive Complexity of Maximizing a Submodular Function</title>
    <dc:date>2018-04-03T19:35:22+00:00</dc:date>
    <link>http://ericbalkanski.com/wp-content/uploads/2018/03/The-Adaptive-Complexity-of-Maximizing-a-Submodular-Function.pdf</link>
    <dc:creator>arsyed</dc:creator><dc:subject>submodularity complexity adaptive</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:324d46f3c90c/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1108.1791">
    <title>[1108.1791] Why Philosophers Should Care About Computational Complexity</title>
    <dc:date>2018-01-04T02:59:26+00:00</dc:date>
    <link>https://arxiv.org/abs/1108.1791</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["One might think that, once we know something is computable, how efficiently it can be computed is a practical question with little further philosophical importance. In this essay, I offer a detailed case that one would be wrong. In particular, I argue that computational complexity theory---the field that studies the resources (such as time, space, and randomness) needed to solve computational problems---leads to new perspectives on the nature of mathematical knowledge, the strong AI debate, computationalism, the problem of logical omniscience, Hume's problem of induction, Goodman's grue riddle, the foundations of quantum mechanics, economic rationality, closed timelike curves, and several other topics of philosophical interest. I end by discussing aspects of complexity theory itself that could benefit from philosophical analysis."]]></description>
<dc:subject>papers philosophy complexity compsci time</dc:subject>
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<dc:identifier>https://pinboard.in/u:arsyed/b:c9afa0527d2f/</dc:identifier>
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    <title>Fun with Parameterized Complexity</title>
    <dc:date>2017-07-20T17:50:26+00:00</dc:date>
    <link>http://tcs.rwth-aachen.de/~sanchez/slides/Raleigh2014.pdf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["About ten years ago, some computer scientists came by and said they heard we have some really cool problems. They showed that the problems are NP-complete and went away!  —Joseph Felsenstein (Molecular biologist)"]]></description>
<dc:subject>complexity compsci</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:f33d2997a099/</dc:identifier>
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<item rdf:about="https://www.cs.toronto.edu/~cebly/Papers/BrafmanEtAl_aaai07.pdf">
    <title>Computing Optimal Subsets</title>
    <dc:date>2017-05-03T03:17:33+00:00</dc:date>
    <link>https://www.cs.toronto.edu/~cebly/Papers/BrafmanEtAl_aaai07.pdf</link>
    <dc:creator>arsyed</dc:creator><dc:subject>papers subset-selection complexity preference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:c7ce78d0e659/</dc:identifier>
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<item rdf:about="https://www.oreilly.com/ideas/the-hard-thing-about-deep-learning">
    <title>The hard thing about deep learning - O'Reilly Media</title>
    <dc:date>2017-01-24T20:58:38+00:00</dc:date>
    <link>https://www.oreilly.com/ideas/the-hard-thing-about-deep-learning</link>
    <dc:creator>arsyed</dc:creator><dc:subject>neural-net optimization complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a32fb42fbe70/</dc:identifier>
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</item>
<item rdf:about="https://www.quora.com/In-deep-learning-why-dont-we-use-the-whole-training-set-to-compute-the-gradient">
    <title>(2) In deep learning, why don't we use the whole training set to compute the gradient? - Quora</title>
    <dc:date>2016-11-06T14:26:52+00:00</dc:date>
    <link>https://www.quora.com/In-deep-learning-why-dont-we-use-the-whole-training-set-to-compute-the-gradient</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[When you put m examples in a minibatch, you need to do O(m) computation and use O(m) memory, but you reduce the amount of uncertainty in the gradient by a factor of only O(sqrt(m)). In other words, there are diminishing marginal returns to putting more examples in the minibatch. You can read more about this in Chapter 8 of the deep learning textbook, on optimization algorithms for deep learning: http://www.deeplearningbook.org/...

]]></description>
<dc:subject>neural-net optimization gradient-descent sgd complexity mini-batch</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:4433551120ad/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1405.4980">
    <title>[1405.4980] Convex Optimization: Algorithms and Complexity</title>
    <dc:date>2016-07-20T06:45:52+00:00</dc:date>
    <link>http://arxiv.org/abs/1405.4980</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural optimization and stochastic optimization. Our presentation of black-box optimization, strongly influenced by Nesterov's seminal book and Nemirovski's lecture notes, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes. We also pay special attention to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror descent, and dual averaging) and discuss their relevance in machine learning. We provide a gentle introduction to structural optimization with FISTA (to optimize a sum of a smooth and a simple non-smooth term), saddle-point mirror prox (Nemirovski's alternative to Nesterov's smoothing), and a concise description of interior point methods. In stochastic optimization we discuss stochastic gradient descent, mini-batches, random coordinate descent, and sublinear algorithms. We also briefly touch upon convex relaxation of combinatorial problems and the use of randomness to round solutions, as well as random walks based methods."]]></description>
<dc:subject>papers optimization convex algorithms complexity</dc:subject>
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<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:convex"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jeremykun.com/2015/11/12/a-quasipolynomial-time-algorithm-for-graph-isomorphism-the-details/">
    <title>A Quasipolynomial Time Algorithm for Graph Isomorphism: The Details | Math ∩ Programming</title>
    <dc:date>2015-11-17T04:33:28+00:00</dc:date>
    <link>http://jeremykun.com/2015/11/12/a-quasipolynomial-time-algorithm-for-graph-isomorphism-the-details/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>algorithms graph isomorphism complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:1f1c52c37b6b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:isomorphism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.4948">
    <title>[1304.4948] On the Approximation of Submodular Functions</title>
    <dc:date>2015-07-01T22:14:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.4948</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Submodular functions are a fundamental object of study in combinatorial optimization, economics, machine learning, etc. and exhibit a rich combinatorial structure. Many subclasses of submodular functions have also been well studied and these subclasses widely vary in their complexity. Our motivation is to understand the relative complexity of these classes of functions. Towards this, we consider the question of how well can one class of submodular functions be approximated by another (simpler) class of submodular functions. Such approximations naturally allow algorithms designed for the simpler class to be applied to the bigger class of functions. We prove both upper and lower bounds on such approximations. 
Our main results are: 
1. General submodular functions can be approximated by cut functions of directed graphs to a factor of n2/4, which is tight. 
2. General symmetric submodular functions1 can be approximated by cut functions of undirected graphs to a factor of n−1, which is tight up to a constant. 
3. Budgeted additive functions can be approximated by coverage functions to a factor of e/(e−1), which is tight. 
Here n is the size of the ground set on which the submodular function is defined. 
We also observe that prior works imply that monotone submodular functions can be approximated by coverage functions with a factor between O(n‾√logn) and Ω(n1/3/log2n)."]]></description>
<dc:subject>papers algorithms approximation complexity submodularity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e9c9072ac807/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:submodularity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1502.01063">
    <title>[1502.01063] Quadratic Conditional Lower Bounds for String Problems and Dynamic Time Warping</title>
    <dc:date>2015-04-10T13:18:24+00:00</dc:date>
    <link>http://arxiv.org/abs/1502.01063</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA[Classic similarity measures of strings are longest common subsequence and Levenshtein distance (i.e., the classic edit distance). A classic similarity measure of curves is dynamic time warping. These measures can be computed by simple O(n2) dynamic programming algorithms, and despite much effort no algorithms with significantly better running time are known. 
We prove that, even restricted to binary strings or one-dimensional curves, respectively, these measures do not have strongly subquadratic time algorithms, i.e., no algorithms with running time O(n2−ε) for any ε>0, unless the Strong Exponential Time Hypothesis fails. We generalize the result to edit distance for arbitrary fixed costs of the four operations (deletion in one of the two strings, matching, substitution), by identifying trivial cases that can be solved in constant time, and proving quadratic-time hardness on binary strings for all other cost choices. This improves and generalizes the known hardness result for Levenshtein distance [Backurs, Indyk STOC'15] by the restriction to binary strings and the generalization to arbitrary costs, and adds important problems to a recent line of research showing conditional lower bounds for a growing number of quadratic time problems. 
As our main technical contribution, we introduce a framework for proving quadratic-time hardness of similarity measures. To apply the framework it suffices to construct a single gadget, which encapsulates all the expressive power necessary to emulate a reduction from satisfiability. 
Finally, we prove quadratic-time hardness for longest palindromic subsequence and longest tandem subsequence via reductions from longest common subsequence, showing that conditional lower bounds based on the Strong Exponential Time Hypothesis also apply to string problems that are not necessarily similarity measures.
]]></description>
<dc:subject>papers strings algorithms dtw alignment similarity visualization complexity bounds lower-bound via:Vaguery</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:dd6cdfab8fc5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:strings"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:dtw"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:alignment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:similarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:bounds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:lower-bound"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:Vaguery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.0516">
    <title>[1003.0516] Model Selection with the Loss Rank Principle</title>
    <dc:date>2015-02-14T18:37:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.0516</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. We suggest a novel principle - the Loss Rank Principle (LoRP) - for model selection in regression and classification. It is based on the loss rank, which counts how many other (fictitious) data would be fitted better. LoRP selects the model that has minimal loss rank. Unlike most penalized maximum likelihood variants (AIC, BIC, MDL), LoRP depends only on the regression functions and the loss function. It works without a stochastic noise model, and is directly applicable to any non-parametric regressor, like kNN."]]></description>
<dc:subject>papers statistics model-selection complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:d2625f9e7f90/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:model-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/math/0702804">
    <title>[math/0702804] The Loss Rank Principle for Model Selection</title>
    <dc:date>2015-02-14T18:36:09+00:00</dc:date>
    <link>http://arxiv.org/abs/math/0702804</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["We introduce a new principle for model selection in regression and classification. Many regression models are controlled by some smoothness or flexibility or complexity parameter c, e.g. the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. Let f_D^c be the (best) regressor of complexity c on data D. A more flexible regressor can fit more data D' well than a more rigid one. If something (here small loss) is easy to achieve it's typically worth less. We define the loss rank of f_D^c as the number of other (fictitious) data D' that are fitted better by f_D'^c than D is fitted by f_D^c. We suggest selecting the model complexity c that has minimal loss rank (LoRP). Unlike most penalized maximum likelihood variants (AIC,BIC,MDL), LoRP only depends on the regression function and loss function. It works without a stochastic noise model, and is directly applicable to any non-parametric regressor, like kNN. In this paper we formalize, discuss, and motivate LoRP, study it for specific regression problems, in particular linear ones, and compare it to other model selection schemes."]]></description>
<dc:subject>papers statistics model-selection complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:dc7ea5e96301/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:model-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1111.0952">
    <title>[1111.0952] Computing a Nonnegative Matrix Factorization -- Provably</title>
    <dc:date>2014-05-20T15:54:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1111.0952</link>
    <dc:creator>arsyed</dc:creator><dc:subject>sanjeev-arora arxiv research-article nmf linear-algebra optimization machinelearning computerscience complexity via:arthegall</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:b0dc18cbf7d5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sanjeev-arora"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:nmf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:linear-algebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:computerscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:arthegall"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s10618-013-0312-3">
    <title>CID: an efficient complexity-invariant distance for time series - Springer</title>
    <dc:date>2014-03-10T16:02:02+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s10618-013-0312-3</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases."]]></description>
<dc:subject>papers classification time-series distance complexity .se</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:0b75b69ec9ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:time-series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:distance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:.se"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/cs.DS/0111050">
    <title>[cs/0111050] Smoothed Analysis of Algorithms: Why the Simplex Algorithm Usually Takes Polynomial Time</title>
    <dc:date>2013-11-04T10:08:39+00:00</dc:date>
    <link>http://arxiv.org/abs/cs.DS/0111050</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["We introduce the smoothed analysis of algorithms, which is a hybrid of the worst-case and average-case analysis of algorithms. In smoothed analysis, we measure the maximum over inputs of the expected performance of an algorithm under small random perturbations of that input. We measure this performance in terms of both the input size and the magnitude of the perturbations. We show that the simplex algorithm has polynomial smoothed complexity."]]></description>
<dc:subject>papers algorithms simplex complexity optimization linear-programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:e460a51b6472/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:simplex"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:linear-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://stochastix.wordpress.com/2012/12/22/sipser-on-p-versus-np/">
    <title>Sipser on P versus NP | Rod Carvalho</title>
    <dc:date>2013-10-23T04:30:40+00:00</dc:date>
    <link>http://stochastix.wordpress.com/2012/12/22/sipser-on-p-versus-np/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>complexity talks videos .watch</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:457cd4530ebb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:talks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:videos"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:.watch"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://java-srv1.mpi-cbg.de/publications/getDocument.html?id=ff8080812daff75c012dc1b7bc10000c">
    <title>Drawing an elephant with four complex parameters (Mayer, Khairy, Howard) [pdf]</title>
    <dc:date>2013-10-05T19:39:12+00:00</dc:date>
    <link>http://java-srv1.mpi-cbg.de/publications/getDocument.html?id=ff8080812daff75c012dc1b7bc10000c</link>
    <dc:creator>arsyed</dc:creator><dc:subject>funny von-neumann models complexity parameters</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:de5a1f0ba72f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:funny"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:von-neumann"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:parameters"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blogs.princeton.edu/imabandit/orf523-the-complexities-of-optimization/">
    <title>ORF523: The complexities of optimization | I’m a bandit</title>
    <dc:date>2013-09-05T22:28:15+00:00</dc:date>
    <link>https://blogs.princeton.edu/imabandit/orf523-the-complexities-of-optimization/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>notes courses optimization machine-learning complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:93130ee8dfa0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:notes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:courses"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://valis.cs.uiuc.edu/~sariel/papers/03/lloyd_kmeans/kmeans.pdf">
    <title>How Fast is the k-means Method? [pdf]</title>
    <dc:date>2013-07-28T21:52:07+00:00</dc:date>
    <link>http://valis.cs.uiuc.edu/~sariel/papers/03/lloyd_kmeans/kmeans.pdf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["We present polynomial upper and lower bounds on the number of iterations performed by the k-means method (a.k.a. Lloyd’s method) for k-means clustering. Our upper bounds are polynomial in the number of points, number of clusters, and the spread of the point set. We also present a lower bound, showing that in the worst case the k-means heuristic needs to perform Ω(n) iterations, for n points on the real line and two centers. Surprisingly, the spread of the point set in this construction is polynomial. This is the ﬁrst construction showing that the k-means heuristic requires more than a polylogarithmic number of iterations. Furthermore, we present two alternative algorithms, with guaranteed performance, which are simple variants of the k-means method. Results of our experimental studies on these algorithms are also presented."]]></description>
<dc:subject>papers k-means clustering complexity algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:1586501ed98b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:k-means"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://complexityzoo.uwaterloo.ca/Complexity_Zoo">
    <title>Complexity Zoo</title>
    <dc:date>2013-06-15T14:38:01+00:00</dc:date>
    <link>https://complexityzoo.uwaterloo.ca/Complexity_Zoo</link>
    <dc:creator>arsyed</dc:creator><dc:subject>ref wikis complexity algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:0656fd5e1e97/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ref"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:wikis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://icr.uni.lu/srdjan.vesic/2012_JELIA_F.pdf">
    <title>Building an Epistemic Logic for Argumentation</title>
    <dc:date>2013-06-03T04:43:42+00:00</dc:date>
    <link>http://icr.uni.lu/srdjan.vesic/2012_JELIA_F.pdf</link>
    <dc:creator>arsyed</dc:creator><dc:subject>papers argumentation complexity epistemic-logic logic belief belief-revision</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:9d3d8a2407b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:epistemic-logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:belief"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:belief-revision"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.0828">
    <title>[1304.0828] Computational Lower Bounds for Sparse PCA</title>
    <dc:date>2013-05-28T22:07:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.0828</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["In the context of sparse principal component detection, we bring evidence towards the existence of a statistical price to pay for computational efficiency. We measure the performance of a test by the smallest signal strength that it can detect and we propose a computationally efficient method based on semidefinite programming. We also prove that the statistical performance of this test cannot be strictly improved by any computationally efficient method. Our results can be viewed as complexity theoretic lower bounds conditionally on the assumptions that some instances of the planted clique problem cannot be solved in randomized polynomial time."]]></description>
<dc:subject>algorithms complexity statcomp machine-learning pca sparsity sparse-pca papers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a77fe105ca59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:pca"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sparsity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sparse-pca"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/aga.pdf">
    <title>Knowledgebase Compilation for Efficient Logical Argumentation [pdf]</title>
    <dc:date>2013-05-28T18:33:16+00:00</dc:date>
    <link>http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/aga.pdf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic-based argumentation is that an argument is a pair〈Φ,α〉where Φ is minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. Different logics are based on different definitions for entailment and consistency, and give us different options for argumentation. For a variety of logics, in particular for classical logic, the computational viability of generating arguments is an issue. Here, we present a solution that involves compiling a knowledgebase ∆ based on the set of minimal inconsistent subsets of ∆, and then generating arguments from the compilation. Whilst generating a compilation is expensive, generating arguments from a compilation is relatively inexpensive."]]></description>
<dc:subject>papers argumentation kb construction complexity algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:77524a2be177/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:kb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:construction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.lamsade.dauphine.fr/scripts/FILES/publi1754.pdf">
    <title>Valued-Based Argumentation for Tree-like Value Graphs [pdf]</title>
    <dc:date>2013-05-28T18:30:16+00:00</dc:date>
    <link>http://www.lamsade.dauphine.fr/scripts/FILES/publi1754.pdf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["We consider value-based argumentation frameworks (VAFs) introduced
by Bench-Capon (J. Logic Comput.13, 2003), which has been established as a fruitful model to study abstract argumentation systems. It takes into account the relative importance among arguments which reflects the value system of an audience. The central issue in the study of VAFs is the decision problems of subjective acceptance and objective acceptance: an argument is subjectively (objectively, resp.) accepted
if it is accepted with respect to one audience (all possible audiences, resp.) An important limitation for using VAFs in real-world applications is the computational intractability of the acceptance problems. We identify nontrivial classes in terms
of structural restrictions on the underlying graph structure of VAFs and present a polynomial-time algorithm in the spirit of dynamic programming. We supplement the tractability by the hardness result. This extends and generalize the results of Dunne (COMMA 2010) and Kim et al. (Artificial Intelligence 175, 2011)"]]></description>
<dc:subject>argumentation vaf algorithms complexity graph-theory tree decomposition papers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:9c0738f11323/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:vaf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:tree"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:decomposition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1104.4290">
    <title>[1104.4290] Algorithms and Complexity Results for Persuasive Argumentation</title>
    <dc:date>2013-05-28T18:27:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1104.4290</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["The study of arguments as abstract entities and their interaction as introduced by Dung (Artificial Intelligence 177, 1995) has become one of the most active research branches within Artificial Intelligence and Reasoning. A main issue for abstract argumentation systems is the selection of acceptable sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J. Logic Comput. 13, 2003), extends Dung's framework. It takes into account the relative strength of arguments with respect to some ranking representing an audience: an argument is subjectively accepted if it is accepted with respect to some audience, it is objectively accepted if it is accepted with respect to all audiences. Deciding whether an argument is subjectively or objectively accepted, respectively, are computationally intractable problems. In fact, the problems remain intractable under structural restrictions that render the main computational problems for non-value-based argumentation systems tractable. In this paper we identify nontrivial classes of value-based argumentation systems for which the acceptance problems are polynomial-time tractable. The classes are defined by means of structural restrictions in terms of the underlying graphical structure of the value-based system. Furthermore we show that the acceptance problems are intractable for two classes of value-based systems that where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007)."]]></description>
<dc:subject>argumentation algorithms complexity papers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:a397b2136d5b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/chapter/10.1007%2F978-0-387-98197-0_5">
    <title>Complexity of Abstract Argumentation (Paul E. Dunne, Michael Wooldridge)</title>
    <dc:date>2013-05-28T15:48:50+00:00</dc:date>
    <link>http://link.springer.com/chapter/10.1007%2F978-0-387-98197-0_5</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["The semantic models discussed in Chapter 11 provide an important element of the formal computational theory of abstract argumentation. Such models offer a variety of interpretations for “collection of acceptable arguments” but are unconcerned with issues relating to their implementation. In other words, the extension-based semantics described earlier distinguish different views of what it means for a set, S, of arguments to be acceptable, but do not consider the procedures by which such a set might be identified."]]></description>
<dc:subject>papers complexity argumentation acceptance algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:f4277e426dcf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:acceptance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.1883">
    <title>Computational properties of argument systems satisfying graph-theoretic constraints (Paul E. Dunne) [pdf]</title>
    <dc:date>2013-05-28T15:42:43+00:00</dc:date>
    <link>http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.1883</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["One difficulty that arises in abstract argument systems is that many natural questions regarding argument acceptability are, in general, computationally intractable having been classified as complete for classes such as NP, co-NP, and  ¢¡ £. In consequence, a number of researchers have considered methods for specialising the structure of such systems so as to identify classes for which efficient decision processes exist. In this paper the effect of a number of graph-theoretic restrictions is considered: ¤-partite systems (¤¦¥¨ § ) in which the set of arguments may be partitioned into ¤ sets each of which is conflict-free; systems in which the numbers of attacks originating from and made upon any argument are bounded; planar systems; and, finally, those of ¤-bounded treewidth. For the class of bipartite graphs, it is shown that determining the acceptability status of a specific argument can be accomplished in polynomial-time under both credulous and sceptical semantics. In addition we establish the existence of polynomial time methods for systems having bounded treewidth when deciding the following: whether a given (set of) arguments is credulously accepted; if the system has a non-empty preferred extension; has a stable extension; is coherent;"]]></description>
<dc:subject>papers argumentation complexity graph-theory acceptance</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:823845dcc96a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:acceptance"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepage.univie.ac.at/wolfgang.dvorak/files/argcomp.pdf">
    <title>Complexity of Semi-Stable and Stage Semantics in Argumentation Frameworks (Wolfgang Dvorak, Stefan Woltran) [pdf]</title>
    <dc:date>2013-05-28T15:39:41+00:00</dc:date>
    <link>http://homepage.univie.ac.at/wolfgang.dvorak/files/argcomp.pdf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["In this work, we answer two questions about the complexity of
semi-stable semantics for abstract argumentation frameworks: we show Π^P_2-completeness for the problem of deciding whether an argument is skeptically accepted, and respectively, Σ^P_2-completeness for the problem of deciding whether an argument is credulously accepted
under the semi-stable semantics. Furthermore, we extend these complexity bounds to the according decision problems for stage semantics and discuss two approaches towards tractability."]]></description>
<dc:subject>papers argumentation complexity semantics acceptance</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:218a8e84dd9a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:argumentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:semantics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:acceptance"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://yinwang0.wordpress.com/2013/04/02/indexing/">
    <title>Why is Indexing Faster Than Binary Search | Surely I Am Joking</title>
    <dc:date>2013-05-25T22:52:56+00:00</dc:date>
    <link>http://yinwang0.wordpress.com/2013/04/02/indexing/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>complexity indexing search binary-search</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:3a6cd02e2e99/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:indexing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:binary-search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://metaoptimize.com/qa/questions/1201/probability-prediction-with-supervised-learning-simple-methods">
    <title>Probability prediction with supervised learning: simple methods - MetaOptimize Q+A</title>
    <dc:date>2013-05-24T13:08:47+00:00</dc:date>
    <link>http://metaoptimize.com/qa/questions/1201/probability-prediction-with-supervised-learning-simple-methods</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["I was reading a paper on designing SVM's in the primal where in the paper and the MATLAB implementation actually just consider the loss for the points where y(w.x)<1 (Even for the linear SVM's.) that is the support vectors, by which I meant the "number of points". Please correct me if I am wrong here again or you think If I am reading the MATLAB implementation of this paper incorrectly. But I feel pretty sure that that is what is done here.

Please see the implementation corresponding to the paper "Training a Support Vector Machine in the Primal" Code in question is on Line# 168 thru 172 and 101,102.
(Jul 18 '10 at 14:06) kpx

Yes, I really like that "training a support vector machine in the primal" paper. Another, similar, faster method is Pegasos: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.9629&rep=rep1&type=pdf and http://astro.temple.edu/~tua63862/ICML10_BPegasos.pdf .

Yes it's true that the support vector machine loss only depends on the margin errors (the points for which y(w·x+b) < 1), but this doesn't necessarily make it more stable or faster, in the linear case, since you still have to run the algorithm on all points (to classify them and see if they are margin errors) and your final feature vector will have a fixed dimension. Most of the gain in generalisation performance obtained by using an SVM is due to the strong regularization built in the method. Unregularized kernel classifiers would be too unreliable to work, for example, but in SVMs kernels are very practical and have decent generalization performance.

So, yes, the algorithm will focus on the margin errors and optimize around that, but it still has to check all points every once in a while (to see if they became margin errors)."]]></description>
<dc:subject>svm regularization logistic-regression margin complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:960330ad7797/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:svm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:regularization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:logistic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:margin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bigocheatsheet.com/">
    <title>Big-O Algorithm Complexity Cheat Sheet</title>
    <dc:date>2013-05-06T16:51:34+00:00</dc:date>
    <link>http://bigocheatsheet.com/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["A table of big-O values for common algorithms."]]></description>
<dc:subject>algorithms via:mcherm ref cheat-sheets complexity big-oh</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:a83b61a461a5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:mcherm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:ref"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:cheat-sheets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:big-oh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://shaffner.us/cs/papers/tarpit.pdf">
    <title>Out of the Tar Pit (Ben Moseley, Peter Marks) [pdf]</title>
    <dc:date>2013-04-30T05:08:29+00:00</dc:date>
    <link>http://shaffner.us/cs/papers/tarpit.pdf</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Complexity is the single major difficulty in the successful develop ment of large-scale software systems. Following Brooks we distinguish accidental from essential difficulty, but disagree with his premise that most complexity remaining in contemporary systems is essential. We identify common causes of complexity and discuss general approaches which can be taken to eliminate them where they are accidental in nature. To make things more concrete we then give an outline for a potential complexity-minimizing approach based on functional programming and Codd’s relational model of data."]]></description>
<dc:subject>swdev programming complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:5172a706a3a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:swdev"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.quora.com/For-those-who-have-used-neural-networks-or-other-machine-learning-how-much-processing-time-has-it-taken-to-train-them-and-how-large-was-the-dataset">
    <title>For those who have used neural networks or other machine learning, how much processing time has it taken to train them and how large was the dataset? - Quora</title>
    <dc:date>2013-03-04T05:03:05+00:00</dc:date>
    <link>http://www.quora.com/For-those-who-have-used-neural-networks-or-other-machine-learning-how-much-processing-time-has-it-taken-to-train-them-and-how-large-was-the-dataset</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Anywhere from minutes to days depending on the size of training set (from thousands to hundreds of millions of examples) and the number of parameters (from thousands to millions). Basically, each parameter needs to be visited twice for each example. So if you have 1M parameters and 10M examples, each training epoch will require on the order of 20 billion multiply-adds, and in that example the number of training epochs may be on the order of 10. The core computational complexity is a matrix-vector of matrix-matrix product, for which computational costs are easy to obtain for various types of hardware. Keep in mind that memory access could be a bottleneck on some architectures (you really need parameters to hold in memory, and you can gain some speed if the data also hold in memory). Nowadays, many practitioners use GPU, on which these kind of operations can be performed at least 10x faster (in theory much more...). Theano implements compilation of such python-level symbolic expressions to GPU, and as noted above, there are easy to use tutorial-like neural net implementations in Theano"]]></description>
<dc:subject>deep-learning neural-net complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:11e9eec1a728/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:neural-net"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.machinedlearnings.com/2012/12/do-you-really-have-big-data.html">
    <title>Do you really have big data? (Paul Mineiro)</title>
    <dc:date>2013-01-23T00:55:20+00:00</dc:date>
    <link>http://www.machinedlearnings.com/2012/12/do-you-really-have-big-data.html</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["The overall message is basically "there's no data like more data" is sometimes misleading because in practice there is a computational constraint which affects the types of learning methods that are feasible. Sometimes throwing away data to allow for more complicated learning methods is worth it."

" For mnist it appears that throwing data away to learn a more complicated model is initially a good strategy. In general I expect this to be highly problem dependent, and as a researcher or practitioner it's a good idea to have an intuition about where your preferred approach is likely to be competitive. YMMV."]]></description>
<dc:subject>machine-learning complexity scaling big-data trade-offs</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:1ae913ce6121/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:scaling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:big-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:trade-offs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.berkeley.edu/~bartlett/talks/201207-MMDS.pdf">
    <title>Model Selection and Computational Oracle Inequalities for Large-Scale Problems</title>
    <dc:date>2012-12-28T06:34:34+00:00</dc:date>
    <link>http://www.stat.berkeley.edu/~bartlett/talks/201207-MMDS.pdf</link>
    <dc:creator>arsyed</dc:creator><dc:subject>statistics model-selection complexity learning-theory via:cshalizi via:arthegall</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:61fe05ec0721/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:model-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:learning-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:arthegall"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.santafe.edu/news/item/announce-mooc/">
    <title>Announcing SFI's new free online courses in complex systems (Santa Fe Institute)</title>
    <dc:date>2012-10-31T21:35:11+00:00</dc:date>
    <link>http://www.santafe.edu/news/item/announce-mooc/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Beginning in early 2013, SFI will offer a series of massive open online courses (MOOCs) in complex systems science.
The first course, "Introduction to Complexity," will be an accessible introduction to the field, with no pre-requisites and no course fees. It is free and open to anyone."]]></description>
<dc:subject>courses complexity santa-fe-institute via:nelson</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:acf65ebdfef2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:courses"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:santa-fe-institute"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:nelson"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://masi.cscs.lsa.umich.edu/~crshalizi/reviews/nature-of-computation.html">
    <title>Review of Moore and Mertens, The Nature of Computation (Cosma Shalizi)</title>
    <dc:date>2012-09-22T16:54:12+00:00</dc:date>
    <link>http://masi.cscs.lsa.umich.edu/~crshalizi/reviews/nature-of-computation.html</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["This is, simply put, the best-written book on the theory of computation I have ever read; one of the best-written mathematical books I have ever read, period." 
[...]
"I have no idea why theoretical computer science insists on using a vocabulary which makes it sound like a bad fantasy novel (i.e., the kind I read). Perhaps it's some lingering influence of Norbert Wiener?"  (re "adversary')]]></description>
<dc:subject>books rec computation complexity reviews</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:2b971cf9cd5f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:rec"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:reviews"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://discrete.gr/complexity/">
    <title>A Gentle Introduction to Algorithm Complexity Analysis</title>
    <dc:date>2012-07-09T21:41:32+00:00</dc:date>
    <link>http://discrete.gr/complexity/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>algorithms complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:386debd5c02f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.johndcook.com/blog/2012/05/25/unix-doesnt-follow-the-unix-philosophy/">
    <title>Unix doesn’t follow the Unix philosophy (John Cook)</title>
    <dc:date>2012-05-26T17:22:05+00:00</dc:date>
    <link>http://www.johndcook.com/blog/2012/05/25/unix-doesnt-follow-the-unix-philosophy/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["I agree that “all those little design decisions actually matter, and there were places where we could have stopped and said ‘no, don’t do this.’” Some complexity has come from a lack of foresight or a lack of courage. But not all of it. Some of it has come from satisfying what complex humans want from their software."]]></description>
<dc:subject>unix design complexity simplicity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:f97d48a97ef9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:unix"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:simplicity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://exploringcomplexity.blogspot.com/2011/12/discussion-of-thecommon-patterns-of.html">
    <title>Discussion of “The Common Patterns of Nature” (by Steve Franks), part 1 (Melanie Mitchell)</title>
    <dc:date>2011-12-20T05:49:54+00:00</dc:date>
    <link>http://exploringcomplexity.blogspot.com/2011/12/discussion-of-thecommon-patterns-of.html</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Next post (or two, or three): More on Steve Frank’s paper:  Understanding distributions via their information content, and the method of maximum entropy.   Our cast of characters widens to Poisson, Exponential, and Gamma distributions.  Where power laws come in.  What all this has to do with scaling, and why we should care."]]></description>
<dc:subject>science complexity statistics information-theory maxent</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:705e733cd609/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:maxent"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://exploringcomplexity.blogspot.com/2011/11/we-need-to-talk-about-scaling.html">
    <title>Exploring Complexity: We Need to Talk About Scaling (Melanie Mitchell)</title>
    <dc:date>2011-12-08T05:36:26+00:00</dc:date>
    <link>http://exploringcomplexity.blogspot.com/2011/11/we-need-to-talk-about-scaling.html</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["In my next several blog posts I want to talk about scaling, especially about the very recent controversies surrounding claims of power-law scaling of particular phenomena [...] All this is going to require some forays into the wild and unruly land of statistics and data analysis. My goal in the next series of posts is to make sense of the following quite important papers in complex systems, which, taken together, form a kind of mini-course on scaling.  Understanding ideas from these papers is essential in one’s education as a complex-systems scientist or informed “consumer” of this field."]]></description>
<dc:subject>complexity scaling power-law via:cshalizi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:08c9a2dc1a93/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:scaling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:power-law"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature-of-computation.org/">
    <title>Nature of Computation (Cristopher Moore and Stephan Mertens)</title>
    <dc:date>2011-08-15T04:44:12+00:00</dc:date>
    <link>http://www.nature-of-computation.org/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["“To put it bluntly: this book rocks! It's 900+ pages of awesome. It somehow manages to combine the fun of a popular book with the intellectual heft of a textbook, so much so that I don't know what to call it (but whatever the genre is, there needs to be more of it!).” —Scott Aaronson"  (ok, i'm sold!)]]></description>
<dc:subject>books computation complexity via:vaguery .want</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:ac0835c8f5b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:vaguery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:.want"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepages.cwi.nl/~paulv/learning.html">
    <title>Publications: Computational machine learning, Information Theory, Statistics, Cognition (Paul Vitanyi)</title>
    <dc:date>2011-06-19T16:08:36+00:00</dc:date>
    <link>http://homepages.cwi.nl/~paulv/learning.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>papers information-theory statistics complexity machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:271a1da235f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.amazon.com/Favorite-Computational-Complexity-Books/lm/22R1UX0Y9YRT2/ref=cm_lm_byauthor_title_full">
    <title>Amazon.com: Favorite Computational Complexity Books</title>
    <dc:date>2011-06-11T17:35:46+00:00</dc:date>
    <link>http://www.amazon.com/Favorite-Computational-Complexity-Books/lm/22R1UX0Y9YRT2/ref=cm_lm_byauthor_title_full</link>
    <dc:creator>arsyed</dc:creator><dc:subject>books compsci complexity computing rec lance-fortnow</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:66af2eaad7c6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:rec"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:lance-fortnow"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.scottaaronson.com/democritus/lec15.html">
    <title>PHYS771 Lecture 15: Computational Learning</title>
    <dc:date>2011-06-10T18:21:16+00:00</dc:date>
    <link>http://www.scottaaronson.com/democritus/lec15.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>learning-theory complexity .read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:352e28f3245c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:learning-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:.read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stephenjaygould.org/library/shermer_contingency.html">
    <title>&quot;Glorious Contingency,&quot; 1999 (Michael Shermer)</title>
    <dc:date>2010-12-23T21:45:39+00:00</dc:date>
    <link>http://www.stephenjaygould.org/library/shermer_contingency.html</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["Me thinks the gentleman doth protest too much. In my opinion, Dennett, and some others who adhere to a strict Darwinian adaptationist program, may be trying to find in nature a nonexisting pattern that shows us—Homo sapiens—as the nearly inevitable result of evolution. Dennett's crane of relentless natural selection is, for him, a skyhook—"a 'mind-first' force or power or process" that, run over and over, would produce us again and again."]]></description>
<dc:subject>evolution adaptation contingency history complexity daniel-dennet stephen-jay-gould</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arsyed/b:28004c85130b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:adaptation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:contingency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:daniel-dennet"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:stephen-jay-gould"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://web.cecs.pdx.edu/~mm/">
    <title>Melanie Mitchell</title>
    <dc:date>2010-12-02T16:30:06+00:00</dc:date>
    <link>http://web.cecs.pdx.edu/~mm/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>people complexity books rec</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:acb0aa44d564/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:rec"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rjlipton.wordpress.com/2009/05/06/worst-case-complexity/">
    <title>Worst Case Complexity (rj lipton)</title>
    <dc:date>2010-11-27T02:47:36+00:00</dc:date>
    <link>http://rjlipton.wordpress.com/2009/05/06/worst-case-complexity/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity worst-case information-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:099473844b64/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:worst-case"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:information-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://stats.stackexchange.com/questions/2828/measures-of-model-complexity">
    <title>Measures of model complexity - Statistical Analysis</title>
    <dc:date>2010-09-23T20:40:31+00:00</dc:date>
    <link>http://stats.stackexchange.com/questions/2828/measures-of-model-complexity</link>
    <dc:creator>arsyed</dc:creator><dc:subject>statistics model-selection cross-validation gdf complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:872907fe3456/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:model-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:cross-validation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:gdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rjlipton.wordpress.com/2010/08/19/projections-can-be-tricky/">
    <title>Projections Can Be Tricky (RJ Lipton)</title>
    <dc:date>2010-09-06T20:19:12+00:00</dc:date>
    <link>http://rjlipton.wordpress.com/2010/08/19/projections-can-be-tricky/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity algorithms projections math</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:a040c17c0f9c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:projections"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:math"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mathoverflow.net/questions/37518/why-do-statistical-randomness-tests-seem-so-ad-hoc">
    <title>Why do statistical randomness tests seem so ad hoc? - MathOverflow</title>
    <dc:date>2010-09-04T20:42:07+00:00</dc:date>
    <link>http://mathoverflow.net/questions/37518/why-do-statistical-randomness-tests-seem-so-ad-hoc</link>
    <dc:creator>arsyed</dc:creator><dc:subject>statistics randomness complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:e44e00628e43/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:randomness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.eccc.uni-trier.de/report/2010/128/">
    <title>The Equivalence of Sampling and Searching (Scott Aaranson)</title>
    <dc:date>2010-08-16T23:20:22+00:00</dc:date>
    <link>http://www.eccc.uni-trier.de/report/2010/128/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["In this paper, we use tools from Kolmogorov complexity and algorithmic information theory to show that sampling and search problems are essentially equivalent. More precisely, for any sampling problem S, there exists a search problem RS such that, if C is any "reasonable" complexity class, then RS is in the search version of C if and only if S is in the sampling version."
]]></description>
<dc:subject>compsci complexity sampling search</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:a1f2e370397a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:search"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://gowers.wordpress.com/2009/09/22/a-conversation-about-complexity-lower-bounds/">
    <title>A conversation about complexity lower bounds (Tim Gowers)</title>
    <dc:date>2010-08-15T21:08:24+00:00</dc:date>
    <link>http://gowers.wordpress.com/2009/09/22/a-conversation-about-complexity-lower-bounds/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity via:arthegall read</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:7ba761f62285/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:via:arthegall"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rjlipton.wordpress.com/2010/06/26/stating-pnp-without-turing-machines/">
    <title>Stating P=NP Without Turing Machines (Dick Lipton)</title>
    <dc:date>2010-07-07T09:47:04+00:00</dc:date>
    <link>http://rjlipton.wordpress.com/2010/06/26/stating-pnp-without-turing-machines/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>computation complexity p=np integer linear programming math</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:2a9cd6052187/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:p=np"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:integer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:linear"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:math"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.shirky.com/weblog/2010/04/the-collapse-of-complex-business-models/">
    <title>The Collapse of Complex Business Models (Clay Shirky)</title>
    <dc:date>2010-04-03T02:05:30+00:00</dc:date>
    <link>http://www.shirky.com/weblog/2010/04/the-collapse-of-complex-business-models/</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["But there is one compensating advantage for the people who escape the old system: when the ecosystem stops rewarding complexity, it is the people who figure out how to work simply in the present, rather than the people who mastered the complexities of the past, who get to say what happens in the future."
]]></description>
<dc:subject>business media internet complexity sociology history clay-shirky</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:9c8e97034b10/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:business"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:internet"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:clay-shirky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rjlipton.wordpress.com/2010/01/12/a-course-on-complexity-theorem/">
    <title>A Course on Complexity Theory (Dick Lipton)</title>
    <dc:date>2010-01-14T02:47:36+00:00</dc:date>
    <link>http://rjlipton.wordpress.com/2010/01/12/a-course-on-complexity-theorem/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:7bd001b3f4c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://backreaction.blogspot.com/2008/03/book-review-complex-adaptive-systems-by.html">
    <title>Book review: Complex Adaptive Systems, by John Miller and Scott Page (Backreaction)</title>
    <dc:date>2009-10-07T05:20:19+00:00</dc:date>
    <link>http://backreaction.blogspot.com/2008/03/book-review-complex-adaptive-systems-by.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>books reviews complexity adaptive modeling social agents</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:9de8f4db8b5b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:agents"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://hunch.net/~jl/projects/prediction_bounds/thesis/mathml/thesis.xml">
    <title>Quantitatively Tight Sample Complexity Bounds (John Langford)</title>
    <dc:date>2009-08-05T19:29:49+00:00</dc:date>
    <link>http://hunch.net/~jl/projects/prediction_bounds/thesis/mathml/thesis.xml</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["I present many new results on sample complexity bounds (bounds on the future error rate of arbitrary learning algorithms). Of theoretical interest are qualitative and quantitative improvements in sample complexity bounds as well as some techniques and criteria for judging the tightness of sample complexity bounds. On the practical side, I show quantitative results (with true error rate bounds sometimes less than 0.01) for decision trees and neural networks with these sample complexity bounds applied to real world problems. I also present a technique for using both sample complexity bounds and (more traditional) holdout techniques.  Together, the theoretical and practical results of this thesis provide a well-founded practical method for evaluating learning algorithm performance based upon both
training and testing set performance."
]]></description>
<dc:subject>machine-learning theory complexity boundsd</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:e9bff23fded0/</dc:identifier>
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<item rdf:about="http://terrytao.wordpress.com/2009/08/01/pnp-relativisation-and-multiple-choice-exams/">
    <title>P=NP, relativisation, and multiple choice exams (Terrence Tao)</title>
    <dc:date>2009-08-04T05:38:07+00:00</dc:date>
    <link>http://terrytao.wordpress.com/2009/08/01/pnp-relativisation-and-multiple-choice-exams/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>math compsci complexity p=np</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:13470438e1b4/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:p=np"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://wiki.python.org/moin/TimeComplexity">
    <title>TimeComplexity - PythonInfo Wiki</title>
    <dc:date>2009-07-12T20:15:49+00:00</dc:date>
    <link>http://wiki.python.org/moin/TimeComplexity</link>
    <dc:creator>arsyed</dc:creator><description><![CDATA["This page documents the time-complexity of various operations in current CPython. "
]]></description>
<dc:subject>python ref algorithms internals complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:862ea7c0ff0f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:python"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:internals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://complexityblog.com/nucleus/index.php">
    <title>Complexity Blog</title>
    <dc:date>2009-07-01T14:29:29+00:00</dc:date>
    <link>http://complexityblog.com/nucleus/index.php</link>
    <dc:creator>arsyed</dc:creator><dc:subject>blogs complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:3b759209cb72/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:blogs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.computationalcomplexity.org/2009/05/foundations-of-complexity.html">
    <title>Foundations of Complexity (Lance Fortnow)</title>
    <dc:date>2009-06-20T10:24:14+00:00</dc:date>
    <link>http://blog.computationalcomplexity.org/2009/05/foundations-of-complexity.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity read</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:b91818bead30/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.computationalcomplexity.org/2006/12/favorite-theorems-second-decade-recap.html">
    <title>Favorite Theorems: Second Decade Recap (Lance Fortnow)</title>
    <dc:date>2009-06-20T10:22:22+00:00</dc:date>
    <link>http://blog.computationalcomplexity.org/2006/12/favorite-theorems-second-decade-recap.html</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity theorems read</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:bf7661d62187/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:compsci"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:theorems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://yalepress.yale.edu/yupbooks/book.asp?isbn=9780300141733">
    <title>Wetware - Bray, Dennis - Yale University Press</title>
    <dc:date>2009-05-28T02:06:01+00:00</dc:date>
    <link>http://yalepress.yale.edu/yupbooks/book.asp?isbn=9780300141733</link>
    <dc:creator>arsyed</dc:creator><dc:subject>books rec biology computation complexity</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:135eb5542a8e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:rec"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arsyed/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://oscarbonilla.com/2008/11/simple-guide-to-complexity-theory/">
    <title>Simple Guide to Complexity Theory (Oscar Bonilla)</title>
    <dc:date>2009-05-13T02:09:03+00:00</dc:date>
    <link>http://oscarbonilla.com/2008/11/simple-guide-to-complexity-theory/</link>
    <dc:creator>arsyed</dc:creator><dc:subject>compsci complexity np</dc:subject>
<dc:identifier>https://pinboard.in/u:arsyed/b:6059c707ab5a/</dc:identifier>
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