1,773 research outputs found

    Approximation of non-boolean 2CSP

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    We develop a polynomial time Ω(1RlogR)\Omega\left ( \frac 1R \log R \right) approximate algorithm for Max 2CSP-RR, the problem where we are given a collection of constraints, each involving two variables, where each variable ranges over a set of size RR, and we want to find an assignment to the variables that maximizes the number of satisfied constraints. Assuming the Unique Games Conjecture, this is the best possible approximation up to constant factors. Previously, a 1/R1/R-approximate algorithm was known, based on linear programming. Our algorithm is based on semidefinite programming (SDP) and on a novel rounding technique. The SDP that we use has an almost-matching integrality gap

    How to Play Unique Games against a Semi-Random Adversary

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    In this paper, we study the average case complexity of the Unique Games problem. We propose a natural semi-random model, in which a unique game instance is generated in several steps. First an adversary selects a completely satisfiable instance of Unique Games, then she chooses an epsilon-fraction of all edges, and finally replaces ("corrupts") the constraints corresponding to these edges with new constraints. If all steps are adversarial, the adversary can obtain any (1-epsilon) satisfiable instance, so then the problem is as hard as in the worst case. In our semi-random model, one of the steps is random, and all other steps are adversarial. We show that known algorithms for unique games (in particular, all algorithms that use the standard SDP relaxation) fail to solve semi-random instances of Unique Games. We present an algorithm that with high probability finds a solution satisfying a (1-delta) fraction of all constraints in semi-random instances (we require that the average degree of the graph is Omega(log k). To this end, we consider a new non-standard SDP program for Unique Games, which is not a relaxation for the problem, and show how to analyze it. We present a new rounding scheme that simultaneously uses SDP and LP solutions, which we believe is of independent interest. Our result holds only for epsilon less than some absolute constant. We prove that if epsilon > 1/2, then the problem is hard in one of the models, the result assumes the 2-to-2 conjecture. Finally, we study semi-random instances of Unique Games that are at most (1-epsilon) satisfiable. We present an algorithm that with high probability, distinguishes between the case when the instance is a semi-random instance and the case when the instance is an (arbitrary) (1-delta) satisfiable instance if epsilon > c delta

    Collaborative Learning of Stochastic Bandits over a Social Network

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    We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies, and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network, and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks, and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive networkwide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results.Comment: 14 Pages, 6 Figure

    On the Expansion of Group-Based Lifts

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    A kk-lift of an nn-vertex base graph GG is a graph HH on n×kn\times k vertices, where each vertex vv of GG is replaced by kk vertices v1,,vkv_1,\cdots{},v_k and each edge (u,v)(u,v) in GG is replaced by a matching representing a bijection πuv\pi_{uv} so that the edges of HH are of the form (ui,vπuv(i))(u_i,v_{\pi_{uv}(i)}). Lifts have been studied as a means to efficiently construct expanders. In this work, we study lifts obtained from groups and group actions. We derive the spectrum of such lifts via the representation theory principles of the underlying group. Our main results are: (1) There is a constant c1c_1 such that for every k2c1ndk\geq 2^{c_1nd}, there does not exist an abelian kk-lift HH of any nn-vertex dd-regular base graph with HH being almost Ramanujan (nontrivial eigenvalues of the adjacency matrix at most O(d)O(\sqrt{d}) in magnitude). This can be viewed as an analogue of the well-known no-expansion result for abelian Cayley graphs. (2) A uniform random lift in a cyclic group of order kk of any nn-vertex dd-regular base graph GG, with the nontrivial eigenvalues of the adjacency matrix of GG bounded by λ\lambda in magnitude, has the new nontrivial eigenvalues also bounded by λ+O(d)\lambda+O(\sqrt{d}) in magnitude with probability 1keΩ(n/d2)1-ke^{-\Omega(n/d^2)}. In particular, there is a constant c2c_2 such that for every k2c2n/d2k\leq 2^{c_2n/d^2}, there exists a lift HH of every Ramanujan graph in a cyclic group of order kk with HH being almost Ramanujan. We use this to design a quasi-polynomial time algorithm to construct almost Ramanujan expanders deterministically. The existence of expanding lifts in cyclic groups of order k=2O(n/d2)k=2^{O(n/d^2)} can be viewed as a lower bound on the order k0k_0 of the largest abelian group that produces expanding lifts. Our results show that the lower bound matches the upper bound for k0k_0 (upto d3d^3 in the exponent)

    Approaching Author Identity through First-person Pronouns and Metadiscourse : A study of opinion articles in US news media

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    This study combines metadiscourse research and sociolinguistic methods to establish which social variables influence the choice of metadiscourse resources containing first-person pronouns in US opinion news texts. The study has three main goals. The first goal is to establish which first-person pronouns are used by the authors of opinion articles, and which social variables influence or at least correlate with their choice of first-person pronouns the most, as well as to study the contexts in which these pronouns are used. The second goal is to establish which metadiscourse resources and to what extent are used by the authors of different social groups. The third goal is to establish if there is any correlation between various social factors and the use of particular metadiscourse resources. The corpus for the study was collected from articles posted on the sites of eleven US news publishers and consists of op-ed texts on politics and social issues along with the information about the authors of these texts including gender, age, ethnic background, education, and occupation. To fulfill these goals the study uses corpus linguistics methods for calculating and comparing the occurrence frequencies of first-person pronouns by social variables and Ken Hyland's interpersonal model of metadiscourse. The results show that social variables do indeed significantly correlate with the choice of first-person pronouns and the metadiscourse resources containing these pronouns. The pronouns that are mostly used are the subject pronouns I and we, the mostly used metadiscourse resources being Self-mentions and Engagement markers. The most prominent social variables that correlate with the use of pronouns are gender and, to a lesser degree, occupation. The female authors of the articles in the corpus use more first-person pronouns than male authors and show a preference for first-person singular pronouns and plural inclusive pronouns while male authors use more first-person plural pronouns. The most noticeable difference in pronoun usage between genders can be observed between male and female journalists; however, journalists of one gender do not differ from each other in either pronoun or metadiscourse use with other factors being equal
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