Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Universal Clustering via Crowdsourcing   Consider unsupervised clustering of objects drawn from a discrete set,
through the use of human intelligence available in crowdsourcing platforms.
This paper defines and studies the problem of universal clustering using
responses of crowd workers, without knowledge of worker reliability or task
difficulty. We model stochastic worker response distributions by incorporating
traits of memory for similar objects and traits of distance among differing
objects. We are particularly interested in two limiting worker
types---temporary workers who retain no memory of responses and long-term
workers with memory. We first define clustering algorithms for these limiting
cases and then integrate them into an algorithm for the unified worker model.
We prove asymptotic consistency of the algorithms and establish sufficient
conditions on the sample complexity of the algorithm. Converse arguments
establish necessary conditions on sample complexity, proving that the defined
algorithms are asymptotically order-optimal in cost.

8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: eess
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
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The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci