15,094 research outputs found
Gradient descent for sparse rank-one matrix completion for crowd-sourced aggregation of sparsely interacting workers
We consider worker skill estimation for the singlecoin
Dawid-Skene crowdsourcing model. In
practice skill-estimation is challenging because
worker assignments are sparse and irregular due
to the arbitrary, and uncontrolled availability of
workers. We formulate skill estimation as a
rank-one correlation-matrix completion problem,
where the observed components correspond to
observed label correlation between workers. We
show that the correlation matrix can be successfully
recovered and skills identifiable if and only
if the sampling matrix (observed components) is
irreducible and aperiodic. We then propose an
efficient gradient descent scheme and show that
skill estimates converges to the desired global optima
for such sampling matrices. Our proof is
original and the results are surprising in light of
the fact that even the weighted rank-one matrix
factorization problem is NP hard in general. Next
we derive sample complexity bounds for the noisy
case in terms of spectral properties of the signless
Laplacian of the sampling matrix. Our proposed
scheme achieves state-of-art performance on a
number of real-world datasets.Published versio
Decentralized Constraint Satisfaction
We show that several important resource allocation problems in wireless
networks fit within the common framework of Constraint Satisfaction Problems
(CSPs). Inspired by the requirements of these applications, where variables are
located at distinct network devices that may not be able to communicate but may
interfere, we define natural criteria that a CSP solver must possess in order
to be practical. We term these algorithms decentralized CSP solvers. The best
known CSP solvers were designed for centralized problems and do not meet these
criteria. We introduce a stochastic decentralized CSP solver and prove that it
will find a solution in almost surely finite time, should one exist, also
showing it has many practically desirable properties. We benchmark the
algorithm's performance on a well-studied class of CSPs, random k-SAT,
illustrating that the time the algorithm takes to find a satisfying assignment
is competitive with stochastic centralized solvers on problems with order a
thousand variables despite its decentralized nature. We demonstrate the
solver's practical utility for the problems that motivated its introduction by
using it to find a non-interfering channel allocation for a network formed from
data from downtown Manhattan
Software Verification and Graph Similarity for Automated Evaluation of Students' Assignments
In this paper we promote introducing software verification and control flow
graph similarity measurement in automated evaluation of students' programs. We
present a new grading framework that merges results obtained by combination of
these two approaches with results obtained by automated testing, leading to
improved quality and precision of automated grading. These two approaches are
also useful in providing a comprehensible feedback that can help students to
improve the quality of their programs We also present our corresponding tools
that are publicly available and open source. The tools are based on LLVM
low-level intermediate code representation, so they could be applied to a
number of programming languages. Experimental evaluation of the proposed
grading framework is performed on a corpus of university students' programs
written in programming language C. Results of the experiments show that
automatically generated grades are highly correlated with manually determined
grades suggesting that the presented tools can find real-world applications in
studying and grading
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