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    <description>recent bookmarks from mraginsky</description>
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  </channel><item rdf:about="https://arxiv.org/abs/2110.15431">
    <title>[2110.15431] Universal Decision Models</title>
    <dc:date>2022-08-02T21:18:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2110.15431</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Humans are universal decision makers: we reason causally to understand the world; we act competitively to gain advantage in commerce, games, and war; and we are able to learn to make better decisions through trial and error. In this paper, we propose Universal Decision Model (UDM), a mathematical formalism based on category theory. Decision objects in a UDM correspond to instances of decision tasks, ranging from causal models and dynamical systems such as Markov decision processes and predictive state representations, to network multiplayer games and Witsenhausen's intrinsic models, which generalizes all these previous formalisms. A UDM is a category of objects, which include decision objects, observation objects, and solution objects. Bisimulation morphisms map between decision objects that capture structure-preserving abstractions. We formulate universal properties of UDMs, including information integration, decision solvability, and hierarchical abstraction. We describe universal functorial representations of UDMs, and propose an algorithm for computing the minimal object in a UDM using algebraic topology. We sketch out an application of UDMs to causal inference in network economics, using a complex multiplayer producer-consumer two-sided marketplace. ]]></description>
<dc:subject>papers to-read decision-making information-structures game-theory distributed-systems decentralized-control categories</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:28d9e1738a77/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-structures"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
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<item rdf:about="http://simondedeo.com/?p=705">
    <title>The 11th Reason to Delete your Social Media Account: the Algorithm will Find You – Axiom of Chance</title>
    <dc:date>2021-05-03T22:38:35+00:00</dc:date>
    <link>http://simondedeo.com/?p=705</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Counterpoint: it doesn't matter what you do, the Algorithm has already found you.]]></description>
<dc:subject>have-read social-media internet distributed-systems via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:bc5feeabd823/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
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<item rdf:about="https://arxiv.org/abs/1512.02673">
    <title>[1512.02673] Speeding Up Distributed Machine Learning Using Codes</title>
    <dc:date>2017-03-19T03:21:26+00:00</dc:date>
    <link>https://arxiv.org/abs/1512.02673</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[odes are widely used in many engineering applications to offer some form of reliability and fault tolerance. The high-level idea of coding is to exploit resource redundancy to deliver higher robustness against system noise. In large-scale systems there are several types of "noise" that can affect the performance of distributed machine learning algorithms: straggler nodes, system failures, or communication bottlenecks. Moreover, redundancy is abundant: a plethora of nodes, a lot of spare storage, etc. 
In this work, scratching the surface of "codes for distributed computation," we provide theoretical insights on how coded solutions can achieve significant gains compared to uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms: matrix multiplication and data shuffling. For matrix multiplication, we use codes to leverage the plethora of nodes and alleviate the effects of stragglers. We show that if the number of workers is $n$, and the runtime of each subtask has an exponential tail, the optimal coded matrix multiplication is $\Theta(\log n)$ times faster than the uncoded matrix multiplication. In data shuffling, we use codes to exploit the excess in storage and reduce communication bottlenecks. We show that when $\alpha$ is the fraction of the data matrix that can be cached at each worker, and $n$ is the number of workers, coded shuffling reduces the communication cost by a factor $\Theta(\alpha \gamma(n))$ compared to uncoded shuffling, where $\gamma(n)$ is the ratio of the cost of unicasting $n$ messages to $n$ users to broadcasting a common message (of the same size) to $n$ users. Our synthetic and Open MPI experiments on Amazon EC2 show that coded distributed algorithms can achieve significant speedups of up to 40% compared to uncoded distributed algorithms.]]></description>
<dc:subject>papers to-read distributed-systems machine-learning coding-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:260727f7e2df/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1602.07415">
    <title>[1602.07415] Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling</title>
    <dc:date>2017-03-14T15:33:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1602.07415</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Gibbs sampling is a Markov chain Monte Carlo technique commonly used for estimating marginal distributions. To speed up Gibbs sampling, there has recently been interest in parallelizing it by executing asynchronously. While empirical results suggest that many models can be efficiently sampled asynchronously, traditional Markov chain analysis does not apply to the asynchronous case, and thus asynchronous Gibbs sampling is poorly understood. In this paper, we derive a better understanding of the two main challenges of asynchronous Gibbs: bias and mixing time. We show experimentally that our theoretical results match practical outcomes.]]></description>
<dc:subject>papers to-read MCMC distributed-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1f3657321f0a/</dc:identifier>
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<item rdf:about="http://ieeexplore.ieee.org/document/7518670/">
    <title>IEEE Xplore Document - Excess-Risk of Distributed Stochastic Learners</title>
    <dc:date>2016-09-07T02:40:59+00:00</dc:date>
    <link>http://ieeexplore.ieee.org/document/7518670/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[This work studies the learning ability of consensus and diffusion distributed learners from continuous streams of data arising from different but related statistical distributions. Four distinctive features for diffusion learners are revealed in relation to other decentralized schemes even under leftstochastic combination policies. First, closed-form expressions for the evolution of their excess-risk are derived for strongly-convex risk functions under a diminishing step-size rule. Second, using these results, it is shown that the diffusion strategy improves the asymptotic convergence rate of the excess-risk relative to non-cooperative schemes. Third, it is shown that when the innetwork cooperation rules are designed optimally, the performance of the diffusion implementation can outperform that of naive centralized processing. Finally, the arguments further show that diffusion outperforms consensus strategies asymptotically, and that the asymptotic excess-risk expression is invariant to the particular network topology. The framework adopted in this work studies convergence in the stronger mean-squareerror sense, rather than in distribution, and develops tools that enable a close examination of the differences between distributed strategies in terms of asymptotic behavior, as well as in terms of convergence rates.]]></description>
<dc:subject>papers to-read distributed-systems machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:e7174b99d8a0/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1512.09327">
    <title>[1512.09327] Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server</title>
    <dc:date>2016-01-04T16:52:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1512.09327</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Markov chain Monte Carlo (MCMC) sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks.]]></description>
<dc:subject>papers to-read bayesian-learning optimization distributed-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:415fc9e27b77/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1509.04555">
    <title>[1509.04555] Understanding interdependency through complex information sharing</title>
    <dc:date>2015-10-13T03:21:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1509.04555</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The interactions between three or more random variables are often nontrivial, poorly understood, and yet, are paramount for future advances in fields such as network information theory, neuroscience, genetics and many others. In this work, we propose to analyze these interactions as different modes of information sharing. Towards this end, we introduce a novel axiomatic framework for decomposing the joint entropy, which characterizes the various ways in which random variables can share information. The key contribution of our framework is to distinguish between interdependencies where the information is shared redundantly, and synergistic interdependencies where the sharing structure exists in the whole but not between the parts. We show that our axioms determine unique formulas for all the terms of the proposed decomposition for a number of cases of interest. Moreover, we show how these results can be applied to several network information theory problems, providing a more intuitive understanding of their fundamental limits.]]></description>
<dc:subject>papers to-read information-theory complex-systems complexity distributed-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:97ff2c9c459f/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
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<item rdf:about="http://arxiv.org/abs/1404.0145">
    <title>[1404.0145] Distributed Nonlinear Consensus in the Space of Probability Measures</title>
    <dc:date>2014-04-02T02:04:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1404.0145</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Distributed consensus in the Wasserstein metric space of probability measures is introduced for the first time in this work. It is shown that convergence of the individual agents' measures to a common measure value is guaranteed so long as a weak network connectivity condition is satisfied asymptotically. The common measure achieved asymptotically at each agent is the one closest simultaneously to all initial agent measures in the sense that it minimises a weighted sum of Wasserstein distances between it and all the initial measures. This algorithm has applicability in the field of distributed estimation.]]></description>
<dc:subject>papers to-read consensus-algorithms optimal-transportation distributed-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:99635bd4077a/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1305.4548">
    <title>[1305.4548] Distributed Learning of Distributions via Social Sampling</title>
    <dc:date>2013-06-14T01:48:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.4548</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[A protocol for distributed estimation of discrete distributions is proposed. Each agent begins with a single sample from the distribution, and the goal is to learn the empirical distribution of the samples. The protocol is based on a simple message-passing model motivated by communication in social networks. Agents sample a message randomly from their current estimates of the distribution, resulting in a protocol with quantized messages. Using tools from stochastic approximation, the algorithm is shown to converge almost surely. Examples illustrate three regimes with different consensus phenomena. Simulations demonstrate this convergence and give some insight into the effect of network topology.]]></description>
<dc:subject>papers to-read heard-the-talk social-networks distributed-systems stochastic-approximation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:8f207b3532b1/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1208.4415">
    <title>[1208.4415] Distributed Channel Synthesis</title>
    <dc:date>2012-08-23T02:22:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1208.4415</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Two familiar notions of correlation are rediscovered as the extreme operating points for distributed synthesis of a discrete memoryless channel, in which a stochastic channel output is generated based on a description of the channel input. Wyner's common information is the minimum description rate needed. However, when common randomness independent of the input is available, the necessary description rate reduces to Shannon's mutual information. This work characterizes the optimal trade-off between the amount of common randomness used and the required rate of description. We also include a number of related derivations, including the effect of limited local randomness, rate requirements for secrecy, applications to game theory, and new insights into common information duality. 
Our tool of choice for efficiently achieving distributed channel synthesis is the cloud mixing lemma (related to the concept of resolvability of a channel). This work elaborates and generalizes this tool. The direct proof (achievability) constructs a feasible joint distribution over all parts of the system using the cloud mixing lemma, from which the behavior of the encoder and decoder is inferred, with no explicit reference to typicality or binning.]]></description>
<dc:subject>papers to-read information-theory distributed-systems simulation multiterminal-information-theory coordination-via-communication channel-coding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:12351c3c4041/</dc:identifier>
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<item rdf:about="http://www.hks.harvard.edu/davidlazer/files/papers/Lazer_Friedman_ASQ.pdf">
    <title>&quot;The Network Structure of Exploration and Exploitation&quot; (David Lazer and Allan Friedman)</title>
    <dc:date>2012-08-10T01:57:16+00:00</dc:date>
    <link>http://www.hks.harvard.edu/davidlazer/files/papers/Lazer_Friedman_ASQ.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making decentralized-control distributed-systems exploration-vs-exploitation via:cshalizi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:db872e11ad7a/</dc:identifier>
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<item rdf:about="http://www.cscs.umich.edu/~spage/pnas.pdf">
    <title>&quot;Groups of diverse problem solvers can outperform groups of high-ability problem solvers&quot; (Lu Hong and Scott Page)</title>
    <dc:date>2012-08-10T01:55:07+00:00</dc:date>
    <link>http://www.cscs.umich.edu/~spage/pnas.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making distributed-systems networks social-networks via:cshalizi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:561f269c2289/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1107.1222">
    <title>[1107.1222] On the information-theoretic structure of distributed measurements</title>
    <dc:date>2012-04-27T05:17:41+00:00</dc:date>
    <link>http://arxiv.org/abs/1107.1222</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The internal structure of a measuring device, which depends on what its components are and how they are organized, determines how it categorizes its inputs. This paper presents a geometric approach to studying the internal structure of measurements performed by distributed systems such as probabilistic cellular automata. It constructs the quale, a family of sections of a suitably defined presheaf, whose elements correspond to the measurements performed by all subsystems of a distributed system. Using the quale we quantify (i) the information generated by a measurement; (ii) the extent to which a measurement is context-dependent; and (iii) whether a measurement is decomposable into independent submeasurements, which turns out to be equivalent to context-dependence. Finally, we show that only indecomposable measurements are more informative than the sum of their submeasurements.]]></description>
<dc:subject>papers to-read dynamical-systems information-theory complex-systems distributed-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:45eb1b9506e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:complex-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1105.2274">
    <title>[1105.2274] Data-Distributed Weighted Majority and Online Mirror Descent</title>
    <dc:date>2012-02-21T03:26:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1105.2274</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[In this paper, we focus on the question of the extent to which online learning can benefit from distributed computing. We focus on the setting in which $N$ agents online-learn cooperatively, where each agent only has access to its own data. We propose a generic data-distributed online learning meta-algorithm. We then introduce the Distributed Weighted Majority and Distributed Online Mirror Descent algorithms, as special cases. We show, using both theoretical analysis and experiments, that compared to a single agent: given the same computation time, these distributed algorithms achieve smaller generalization errors; and given the same generalization errors, they can be $N$ times faster.]]></description>
<dc:subject>papers to-read machine-learning online-learning optimization distributed-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:2e2387fed77f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1110.3069">
    <title>[1110.3069] Multiterminal Source Coding under Logarithmic Loss</title>
    <dc:date>2011-12-25T03:48:21+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.3069</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read information-theory prediction distributed-systems multiagent-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:3f7dd9792433/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:multiagent-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.podc.org/dijkstra/">
    <title>Edsger W. Dijkstra Prize in Distributed Computing</title>
    <dc:date>2011-11-17T04:53:46+00:00</dc:date>
    <link>http://www.podc.org/dijkstra/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The Edsger W. Dijkstra Prize in Distributed Computing is named for Edsger Wybe Dijkstra (1930-2002), a pioneer in the area of distributed computing. His foundational work on concurrency primitives (such as the semaphore), concurrency problems (such as mutual exclusion and deadlock), reasoning about concurrent systems, and self-stabilization comprises one of the most important supports upon which the field of distributed computing is built. No other individual has had a larger influence on research in principles of distributed computing.]]></description>
<dc:subject>computation distributed-computing distributed-systems reference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:06b7d39d0f56/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6034729">
    <title>IEEE Xplore - Asymptotic Optimality Theory for Decentralized Sequential Multihypothesis Testing Problems</title>
    <dc:date>2011-10-07T00:13:34+00:00</dc:date>
    <link>http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6034729</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making distributed-systems statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:3d460935d6cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1107.2526">
    <title>[1107.2526] On the Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm</title>
    <dc:date>2011-07-14T01:50:40+00:00</dc:date>
    <link>http://arxiv.org/abs/1107.2526</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read distributed-systems optimization consensus-algorithms</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:3de91ad48393/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:consensus-algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ita.ucsd.edu/workshop/11/files/paper/paper_1714.pdf">
    <title>Categorical Decision Making by People, Committees, and Crowds</title>
    <dc:date>2011-04-25T22:23:30+00:00</dc:date>
    <link>http://ita.ucsd.edu/workshop/11/files/paper/paper_1714.pdf</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read decision-making decentralized-control distributed-systems quantization statistics economics filetype:pdf media:document</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1e113f586eaf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:quantization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:filetype:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:media:document"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0912.0338">
    <title>[0912.0338] Correlation Decay in Random Decision Networks</title>
    <dc:date>2010-08-29T04:02:29+00:00</dc:date>
    <link>http://arxiv.org/abs/0912.0338</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers have-read optimization distributed-systems decision-making decentralized-control graph-theory re:distributed_decisions_project</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:482816b4a1ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:re:distributed_decisions_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lccc.eecs.berkeley.edu/">
    <title>LCCC - NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds</title>
    <dc:date>2010-08-20T16:10:34+00:00</dc:date>
    <link>http://lccc.eecs.berkeley.edu/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>conferences learning-theory distributed-systems optimization machine-learning algorithms</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:03024664a095/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:conferences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:learning-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ti.arc.nasa.gov/tech/dash/intelligent-data-understanding/probcol/">
    <title>Probability Collectives (David H. Wolpert)</title>
    <dc:date>2010-07-23T14:27:42+00:00</dc:date>
    <link>http://ti.arc.nasa.gov/tech/dash/intelligent-data-understanding/probcol/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["We recently proved that game theory and statistical physics are identical when cast in terms of information theory.

We call the associated formalism Probability Collectives (PC). PC opens many new lines of research, and provides new approaches to problems in distributed control and distributed optimization."
]]></description>
<dc:subject>research papers reference control-theory distributed-systems decision-making game-theory statistical-physics optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:50763dc23280/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistical-physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepages.cwi.nl/~schuppen/cwi/semcst2008f.html">
    <title>CWI Seminar Control and System Theory - 2008 Fall</title>
    <dc:date>2010-07-07T22:31:39+00:00</dc:date>
    <link>http://homepages.cwi.nl/~schuppen/cwi/semcst2008f.html</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Nice list of references on decentralized control and team decision problems.
]]></description>
<dc:subject>reference control-theory decentralized-control decision-making game-theory distributed-systems</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:cd12fe79f089/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.4039">
    <title>[1006.4039] Cooperative Autonomous Online Learning</title>
    <dc:date>2010-06-23T16:54:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.4039</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Abstract: "Online learning is becoming increasingly popular for training on large datasets. However, the sequential nature of online learning requires a centralized learner to store data and update parameters. In this paper, we consider a fully decentralized setting, cooperative autonomous online learning, with a distributed data source. The learners perform learning with local parameters while periodically communicating with a small subset of neighbors to exchange information. We define the regret in terms of an implicit aggregated parameter of the learners for such a setting and prove regret bounds similar to the classical sequential online learning." Damn, looks like I've been scooped!
]]></description>
<dc:subject>papers have-read decision-making online-learning distributed-systems</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:5098c941c00f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nber.org/papers/w14040">
    <title>Bayesian Learning in Social Networks</title>
    <dc:date>2010-04-24T16:55:28+00:00</dc:date>
    <link>http://www.nber.org/papers/w14040</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Daron Acemoglu, Munther A. Dahleh, Ilan Lobel, Asuman Ozdaglar; NBER Working Paper No. 14040
]]></description>
<dc:subject>papers have-read social-networks bayesian-learning game-theory decision-making distributed-systems</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:460cb76b6994/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:bayesian-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lairlab.org/ispace/">
    <title>Reasoning in Reduced Information Spaces</title>
    <dc:date>2010-04-03T17:56:30+00:00</dc:date>
    <link>http://lairlab.org/ispace/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["...a website for research and coordination on Decentralized Techniques for Reasoning in Reduced Information Spaces (ONR 2009 Multi-disciplinary University Research Project)"
]]></description>
<dc:subject>decision-making decentralized-control machine-learning distributed-systems AI algorithms optimization</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9193757c612a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decentralized-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.prism.gatech.edu/~jshamma3/">
    <title>Jeff Shamma - Georgia Tech</title>
    <dc:date>2009-11-24T02:06:22+00:00</dc:date>
    <link>http://www.prism.gatech.edu/~jshamma3/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>people homepages control-theory game-theory multiagent-systems distributed-systems papers research</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:c9043fc59544/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:homepages"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:multiagent-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:research"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0911.3357">
    <title>[0911.3357] Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks and Computation</title>
    <dc:date>2009-11-18T01:28:20+00:00</dc:date>
    <link>http://arxiv.org/abs/0911.3357</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Nikolaos M. Freris, Hemant Kowshik, P. R. Kumar
]]></description>
<dc:subject>papers to-read sensor-networks distributed-systems optimization information-theory algorithms</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:1efdbab838ad/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:sensor-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0910.2065v1">
    <title>[0910.2065v1] Decentralized Multi-Armed Bandit with Multiple Distributed Players (Keqin Liu, Qing Zhao)</title>
    <dc:date>2009-11-04T14:25:41+00:00</dc:date>
    <link>http://arxiv.org/abs/0910.2065v1</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[From the abstract: "We consider multi-armed bandit with distributed players, where each player independently samples one of N stochastic processes with unknown parameters and accrues reward in each slot without information exchange. Users choosing the same arm collide, and none or only one receives reward depending on the collision model. This problem can be formulated as a decentralized multi-armed bandit problem."
]]></description>
<dc:subject>papers to-read distributed-systems algorithms optimization decision-making decentralized-control control-theory</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9251a2a86a5a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:distributed-systems"/>
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