Distributed Exploration in Multi-Armed Bandits

We study exploration in Multi-Armed Bandits in a setting where $k$ players collaborate in order to identify an $\epsilon$-optimal arm. Our motivation comes from recent employment of bandit algorithms in computationally intensive, large-scale applications. Our results demonstrate a non-trivial tradeoff between the number of arm pulls required by each of the players, and the amount of communication between them. In particular, our main result shows that by allowing the $k$ players to communicate only once, they are able to learn $\sqrt{k}$ times faster than a single player. That is, distributing learning to $k$ players gives rise to a factor $\sqrt{k}$ parallel speed-up. We complement this result with a lower bound showing this is in general the best possible. On the other extreme, we present an algorithm that achieves the ideal factor $k$ speed-up in learning performance, with communication only logarithmic in $1/\epsilon$

The Mayor\u27s Henchmen and Henchwomen, Both White and Colored : Edward H. Armstrong and the Politics of Race in Daytona Beach, 1900-1940

Writing about his early twentieth-century childhood in Daytona, the renowned theologian Howard Thurman related a story about his older cousin and idol. Thorton Smith. A semi-pro baseball player in his youth, Smith had established himself by the 1920s as a successful restaurateur in Midway, the most business-oriented of Daytona\u27s three black neighoorhoods. He purchased supplies from Edward Armstrong, a white grocer and aspiring politician who wanted to loosen the Ku Klux Klan\u27s grip on the city; Smith suggested to him that the Klan could be defeated only if blacks were allowed to vote. After in itially rejecting the idea, the grocer and his politicai allies finally agreed to grant the franchise to black property owners and taxpayers, and the biracial alliance eventually managed to unseat the Klan. After thanking Smith, Armstrong offered tile black businessman an envelope stuffed with cash, which Smith rejected. Instead, he demanded and received from the Armstrong faction a new school for black children and uniformed black policemen to patrol African American neighborhoods

Economic cost and health care workforce effects of school closures in the U.S

School closure is an important component of U.S. pandemic flu mitigation strategy. The benefit is a reduction in epidemic severity through reduction in school-age contacts. However, school closure involves two types of cost. First is the direct economic impact of the worker absenteeism generated by school closures. Second, many of the relevant absentees will be health care workers themselves, which will adversely affect the delivery of vaccine and other emergency services. Neither of these costs has been estimated in detail for the United States. We offer detailed estimates, and improve on the methodologies thus far employed in the non-U.S. literature. We give estimates of both the direct economic and health care impacts for school closure durations of 2, 4, 8, and 12 weeks under a range of assumptions. We find that closing all schools in the U.S. for four weeks could cost between 10 and 47 billion dollars (0.1-0.3% of GDP) and lead to a reduction of 6% to 19% in key health care personnel. These should be considered conservative (i.e., low) economic estimates in that earnings rather than total compensation are used to calculate costs. We also provide per student costs, so regionally heterogeneous policies can be evaluated. These estimates permit the epidemiological benefits of school closure to be compared to the costs at multiple scales and over many durations -- AbstractHoward Lempel, Ross A. Hammond, Joshua M. Epstein."September 30, 2009."Available in PDF from the Brookings Institution website (www.brookings.edu) ; viewed 09/30/09Includes bibliographical referencesThis work was supported by: The Models of Infectious Disease Agent Study (MIDAS), under Award Number U01GM070708 from the National Institutes of General Medical Sciences; The University of Pittsburgh Public Health Adaptive Systems (PHASYS) project, under Cooperative Agreement Number 1P01TP000304-01 from the Centers for Disease Control and Prevention; and The Johns Hopkins Medical School Center on Preparedness and Catastrophic Event Response (PACER), under Award Number N00014-06-1-0991 from the Office of Naval Research. Epstein's work is also supported by an NIH Director's Pioneer Award, Number DP1OD003874 from the Office of the Director, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences, the National Institutes of Health, the Centers for Disease Control and Prevention, the Office of Naval Research, or the Office of the Director, National Institutes of Healt

Predictive caching and prefetching of query results in search engines

We study the caching of query result pages in Web search engines. Popular search engines receive millions of queries per day, and ecient policies for caching query results may enable them to lower their response time and reduce their hardware requirements. We present PDC (probability driven cache), a novel scheme tailored for caching search results, that is based on a probabilistic model of search engine users. We then use a trace of over seven million queries submitted to the search engine AltaVista to evaluate PDC, as well as traditional LRU and SLRU based caching schemes. The trace driven simulations show that PDC outperforms the other policies. We also examine the prefetching of search results, and demonstrate that prefetching can increase cache hit ratios by 50% for large caches, and can double the hit ratios of small caches. When integrating prefetching into PDC, we attain hit ratios of over 0:53.

An algorithm for finding a shortest vector in a two-dimensional modular lattice

AbstractLet 0 < a, b < d be integers with a ≠ b. The lattice Ld(a, b) is the set of all multiples of the vector (a, b) modulo d. An algorithm is presented for finding a shortest vector in Ld(a, b). The complexity of the algorithm is shown to be logarithmic in the size of d when the number of arithmetical operations is counted

Social Ranking Techniques for the Web

The proliferation of social media has the potential for changing the structure and organization of the web. In the past, scientists have looked at the web as a large connected component to understand how the topology of hyperlinks correlates with the quality of information contained in the page and they proposed techniques to rank information contained in web pages. We argue that information from web pages and network data on social relationships can be combined to create a personalized and socially connected web. In this paper, we look at the web as a composition of two networks, one consisting of information in web pages and the other of personal data shared on social media web sites. Together, they allow us to analyze how social media tunnels the flow of information from person to person and how to use the structure of the social network to rank, deliver, and organize information specifically for each individual user. We validate our social ranking concepts through a ranking experiment conducted on web pages that users shared on Google Buzz and Twitter.Comment: 7 pages, ASONAM 201

Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure

Optimizing XML Compression

The eXtensible Markup Language (XML) provides a powerful and flexible means of encoding and exchanging data. As it turns out, its main advantage as an encoding format (namely, its requirement that all open and close markup tags are present and properly balanced) yield also one of its main disadvantages: verbosity. XML-conscious compression techniques seek to overcome this drawback. Many of these techniques first separate XML structure from the document content, and then compress each independently. Further compression gains can be realized by identifying and compressing together document content that is highly similar, thereby amortizing the storage costs of auxiliary information required by the chosen compression algorithm. Additionally, the proper choice of compression algorithm is an important factor not only for the achievable compression gain, but also for access performance. Hence, choosing a compression configuration that optimizes compression gain requires one to determine (1) a partitioning strategy for document content, and (2) the best available compression algorithm to apply to each set within this partition. In this paper, we show that finding an optimal compression configuration with respect to compression gain is an NP-hard optimization problem. This problem remains intractable even if one considers a single compression algorithm for all content. We also describe an approximation algorithm for selecting a partitioning strategy for document content based on the branch-and-bound paradigm.Comment: 16 pages, extended version of paper accepted for XSym 200