22,589 research outputs found
Unbiased Comparative Evaluation of Ranking Functions
Eliciting relevance judgments for ranking evaluation is labor-intensive and
costly, motivating careful selection of which documents to judge. Unlike
traditional approaches that make this selection deterministically,
probabilistic sampling has shown intriguing promise since it enables the design
of estimators that are provably unbiased even when reusing data with missing
judgments. In this paper, we first unify and extend these sampling approaches
by viewing the evaluation problem as a Monte Carlo estimation task that applies
to a large number of common IR metrics. Drawing on the theoretical clarity that
this view offers, we tackle three practical evaluation scenarios: comparing two
systems, comparing systems against a baseline, and ranking systems. For
each scenario, we derive an estimator and a variance-optimizing sampling
distribution while retaining the strengths of sampling-based evaluation,
including unbiasedness, reusability despite missing data, and ease of use in
practice. In addition to the theoretical contribution, we empirically evaluate
our methods against previously used sampling heuristics and find that they
generally cut the number of required relevance judgments at least in half.Comment: Under review; 10 page
Active Sampling for Large-scale Information Retrieval Evaluation
Evaluation is crucial in Information Retrieval. The development of models,
tools and methods has significantly benefited from the availability of reusable
test collections formed through a standardized and thoroughly tested
methodology, known as the Cranfield paradigm. Constructing these collections
requires obtaining relevance judgments for a pool of documents, retrieved by
systems participating in an evaluation task; thus involves immense human labor.
To alleviate this effort different methods for constructing collections have
been proposed in the literature, falling under two broad categories: (a)
sampling, and (b) active selection of documents. The former devises a smart
sampling strategy by choosing only a subset of documents to be assessed and
inferring evaluation measure on the basis of the obtained sample; the sampling
distribution is being fixed at the beginning of the process. The latter
recognizes that systems contributing documents to be judged vary in quality,
and actively selects documents from good systems. The quality of systems is
measured every time a new document is being judged. In this paper we seek to
solve the problem of large-scale retrieval evaluation combining the two
approaches. We devise an active sampling method that avoids the bias of the
active selection methods towards good systems, and at the same time reduces the
variance of the current sampling approaches by placing a distribution over
systems, which varies as judgments become available. We validate the proposed
method using TREC data and demonstrate the advantages of this new method
compared to past approaches
Sampling Online Social Networks via Heterogeneous Statistics
Most sampling techniques for online social networks (OSNs) are based on a
particular sampling method on a single graph, which is referred to as a
statistics. However, various realizing methods on different graphs could
possibly be used in the same OSN, and they may lead to different sampling
efficiencies, i.e., asymptotic variances. To utilize multiple statistics for
accurate measurements, we formulate a mixture sampling problem, through which
we construct a mixture unbiased estimator which minimizes asymptotic variance.
Given fixed sampling budgets for different statistics, we derive the optimal
weights to combine the individual estimators; given fixed total budget, we show
that a greedy allocation towards the most efficient statistics is optimal. In
practice, the sampling efficiencies of statistics can be quite different for
various targets and are unknown before sampling. To solve this problem, we
design a two-stage framework which adaptively spends a partial budget to test
different statistics and allocates the remaining budget to the inferred best
statistics. We show that our two-stage framework is a generalization of 1)
randomly choosing a statistics and 2) evenly allocating the total budget among
all available statistics, and our adaptive algorithm achieves higher efficiency
than these benchmark strategies in theory and experiment
Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics
National statistical agencies around the world publish tabular summaries based on combined employer-employee (ER-EE) data. The privacy of both individuals and business establishments that feature in these data are protected by law in most countries. These data are currently released using a variety of statistical disclosure limitation (SDL) techniques that do not reveal the exact characteristics of particular employers and employees, but lack provable privacy guarantees limiting inferential disclosures. In this work, we present novel algorithms for releasing tabular summaries of linked ER-EE data with formal, provable guarantees of privacy. We show that state-of-the-art differentially private algorithms add too much noise for the output to be useful. Instead, we identify the privacy requirements mandated by current interpretations of the relevant laws, and formalize them using the Pufferfish framework. We then develop new privacy definitions that are customized to ER-EE data and satisfy the statutory privacy requirements. We implement the experiments in this paper on production data gathered by the U.S. Census Bureau. An empirical evaluation of utility for these data shows that for reasonable values of the privacy-loss parameter ϵ≥1, the additive error introduced by our provably private algorithms is comparable, and in some cases better, than the error introduced by existing SDL techniques that have no provable privacy guarantees. For some complex queries currently published, however, our algorithms do not have utility comparable to the existing traditiona
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