2,185 research outputs found
Beyond group-level explanations for the failure of groups to solve hidden profiles: The individual preference effect revisited
The individual preference effect supplements the predominant group-level explanations for the failure of groups to solve hidden profiles. Even in the absence of dysfunctional group-level processes, group members tend to stick to their suboptimal initial decision preferences due to preference-consistent evaluation of information. However, previous experiments demonstrating this effect retained two group-level processes, namely (a) social validation of information supporting the group members’ initial preferences and (b) presentation of the additional information in a discussion format. Therefore, it was unclear whether the individual preference effect depends on the co-occurrence of these group-level processes. Here, we report two experiments demonstrating that the individual preference effect is indeed an individual-level phenomenon. Moreover, by a comparison to real interacting groups, we can show that even when all relevant information is exchanged and when no coordination losses occur, almost half of all groups would fail to solve hidden profiles due to the individual preference effect
Marginal Release Under Local Differential Privacy
Many analysis and machine learning tasks require the availability of marginal
statistics on multidimensional datasets while providing strong privacy
guarantees for the data subjects. Applications for these statistics range from
finding correlations in the data to fitting sophisticated prediction models. In
this paper, we provide a set of algorithms for materializing marginal
statistics under the strong model of local differential privacy. We prove the
first tight theoretical bounds on the accuracy of marginals compiled under each
approach, perform empirical evaluation to confirm these bounds, and evaluate
them for tasks such as modeling and correlation testing. Our results show that
releasing information based on (local) Fourier transformations of the input is
preferable to alternatives based directly on (local) marginals
Landau-De Gennes theory of nematic liquid\ud crystals: the Oseen-Frank limit and beyond
We study global minimizers of a continuum Landau-De Gennes energy functional for nematic liquid crystals, in three-dimensional domains, subject to uniaxial boundary conditions. We analyze the physically relevant limit of small elastic constant and show that global minimizers converge strongly, in W 1,2 , to a global minimizer predicted by the Oseen-Frank theory for uniaxial nematic liquid crystals with constant order parameter. Moreover, the convergence is uniform in the interior of the domain, away from the singularities of the limiting Oseen-Frank global minimizer. We obtain results on the rate of convergence of the eigenvalues and the regularity of the eigenvectors of the Landau-De Gennes global minimizer.\ud
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We also study the interplay between biaxiality and uniaxiality in Landau-De Gennes global energy minimizers and obtain estimates for various related quantities such as the biaxiality parameter and the size of admissible strongly biaxial regions
Testing Conditional Independence of Discrete Distributions
We study the problem of testing \emph{conditional independence} for discrete
distributions. Specifically, given samples from a discrete random variable on domain , we want to distinguish,
with probability at least , between the case that and are
conditionally independent given from the case that is
-far, in -distance, from every distribution that has this
property. Conditional independence is a concept of central importance in
probability and statistics with a range of applications in various scientific
domains. As such, the statistical task of testing conditional independence has
been extensively studied in various forms within the statistics and
econometrics communities for nearly a century. Perhaps surprisingly, this
problem has not been previously considered in the framework of distribution
property testing and in particular no tester with sublinear sample complexity
is known, even for the important special case that the domains of and
are binary.
The main algorithmic result of this work is the first conditional
independence tester with {\em sublinear} sample complexity for discrete
distributions over . To complement our upper
bounds, we prove information-theoretic lower bounds establishing that the
sample complexity of our algorithm is optimal, up to constant factors, for a
number of settings. Specifically, for the prototypical setting when , we show that the sample complexity of testing conditional
independence (upper bound and matching lower bound) is
\[
\Theta\left({\max\left(n^{1/2}/\epsilon^2,\min\left(n^{7/8}/\epsilon,n^{6/7}/\epsilon^{8/7}\right)\right)}\right)\,.
\
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
We present a framework for quantifying and mitigating algorithmic bias in
mechanisms designed for ranking individuals, typically used as part of
web-scale search and recommendation systems. We first propose complementary
measures to quantify bias with respect to protected attributes such as gender
and age. We then present algorithms for computing fairness-aware re-ranking of
results. For a given search or recommendation task, our algorithms seek to
achieve a desired distribution of top ranked results with respect to one or
more protected attributes. We show that such a framework can be tailored to
achieve fairness criteria such as equality of opportunity and demographic
parity depending on the choice of the desired distribution. We evaluate the
proposed algorithms via extensive simulations over different parameter choices,
and study the effect of fairness-aware ranking on both bias and utility
measures. We finally present the online A/B testing results from applying our
framework towards representative ranking in LinkedIn Talent Search, and discuss
the lessons learned in practice. Our approach resulted in tremendous
improvement in the fairness metrics (nearly three fold increase in the number
of search queries with representative results) without affecting the business
metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users
worldwide. Ours is the first large-scale deployed framework for ensuring
fairness in the hiring domain, with the potential positive impact for more than
630M LinkedIn members.Comment: This paper has been accepted for publication at ACM KDD 201
Differentially Private Model Selection with Penalized and Constrained Likelihood
In statistical disclosure control, the goal of data analysis is twofold: The
released information must provide accurate and useful statistics about the
underlying population of interest, while minimizing the potential for an
individual record to be identified. In recent years, the notion of differential
privacy has received much attention in theoretical computer science, machine
learning, and statistics. It provides a rigorous and strong notion of
protection for individuals' sensitive information. A fundamental question is
how to incorporate differential privacy into traditional statistical inference
procedures. In this paper we study model selection in multivariate linear
regression under the constraint of differential privacy. We show that model
selection procedures based on penalized least squares or likelihood can be made
differentially private by a combination of regularization and randomization,
and propose two algorithms to do so. We show that our private procedures are
consistent under essentially the same conditions as the corresponding
non-private procedures. We also find that under differential privacy, the
procedure becomes more sensitive to the tuning parameters. We illustrate and
evaluate our method using simulation studies and two real data examples
No measure for culture? Value in the new economy
This paper explores articulations of the value of investment in culture and the arts through a critical discourse analysis of policy documents, reports and academic commentary since 1997. It argues that in this period, discourses around the value of culture have moved from a focus on the direct economic contributions of the culture industries to their indirect economic benefits. These indirect benefits are discussed here under three main headings: creativity and innovation, employability, and social inclusion. These are in turn analysed in terms of three forms of capital: human, social and cultural. The paper concludes with an analysis of this discursive shift through the lens of autonomist Marxist concerns with the labour of social reproduction. It is our argument that, in contemporary policy discourses on culture and the arts, the government in the UK is increasingly concerned with the use of culture to form the social in the image of capital. As such, we must turn our attention beyond the walls of the factory in order to understand the contemporary capitalist production of value and resistance to it. </jats:p
Foundation and empire : a critique of Hardt and Negri
In this article, Thompson complements recent critiques of Hardt and Negri's Empire (see Finn Bowring in Capital and Class, no. 83) using the tools of labour process theory to critique the political economy of Empire, and to note its unfortunate similarities to conventional theories of the knowledge economy
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