129 research outputs found
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
Consider a requester who wishes to crowdsource a series of identical binary
labeling tasks to a pool of workers so as to achieve an assured accuracy for
each task, in a cost optimal way. The workers are heterogeneous with unknown
but fixed qualities and their costs are private. The problem is to select for
each task an optimal subset of workers so that the outcome obtained from the
selected workers guarantees a target accuracy level. The problem is a
challenging one even in a non strategic setting since the accuracy of
aggregated label depends on unknown qualities. We develop a novel multi-armed
bandit (MAB) mechanism for solving this problem. First, we propose a framework,
Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained
Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound
on the number of time steps the algorithm chooses a sub-optimal set that
depends on the target accuracy level and true qualities. A more challenging
situation arises when the requester not only has to learn the qualities of the
workers but also elicit their true costs. We modify the CCB-NS algorithm to
obtain an adaptive exploration separated algorithm which we call { \em
Constrained Confidence Bound for a Strategic setting (CCB-S)}. CCB-S algorithm
produces an ex-post monotone allocation rule and thus can be transformed into
an ex-post incentive compatible and ex-post individually rational mechanism
that learns the qualities of the workers and guarantees a given target accuracy
level in a cost optimal way. We provide a lower bound on the number of times
any algorithm should select a sub-optimal set and we see that the lower bound
matches our upper bound upto a constant factor. We provide insights on the
practical implementation of this framework through an illustrative example and
we show the efficacy of our algorithms through simulations
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing
In mobile crowdsourcing (MCS), mobile users accomplish outsourced human
intelligence tasks. MCS requires an appropriate task assignment strategy, since
different workers may have different performance in terms of acceptance rate
and quality. Task assignment is challenging, since a worker's performance (i)
may fluctuate, depending on both the worker's current personal context and the
task context, (ii) is not known a priori, but has to be learned over time.
Moreover, learning context-specific worker performance requires access to
context information, which may not be available at a central entity due to
communication overhead or privacy concerns. Additionally, evaluating worker
performance might require costly quality assessments. In this paper, we propose
a context-aware hierarchical online learning algorithm addressing the problem
of performance maximization in MCS. In our algorithm, a local controller (LC)
in the mobile device of a worker regularly observes the worker's context,
her/his decisions to accept or decline tasks and the quality in completing
tasks. Based on these observations, the LC regularly estimates the worker's
context-specific performance. The mobile crowdsourcing platform (MCSP) then
selects workers based on performance estimates received from the LCs. This
hierarchical approach enables the LCs to learn context-specific worker
performance and it enables the MCSP to select suitable workers. In addition,
our algorithm preserves worker context locally, and it keeps the number of
required quality assessments low. We prove that our algorithm converges to the
optimal task assignment strategy. Moreover, the algorithm outperforms simpler
task assignment strategies in experiments based on synthetic and real data.Comment: 18 pages, 10 figure
Crowdsourced PAC Learning under Classification Noise
In this paper, we analyze PAC learnability from labels produced by
crowdsourcing. In our setting, unlabeled examples are drawn from a distribution
and labels are crowdsourced from workers who operate under classification
noise, each with their own noise parameter. We develop an end-to-end
crowdsourced PAC learning algorithm that takes unlabeled data points as input
and outputs a trained classifier. Our three-step algorithm incorporates
majority voting, pure-exploration bandits, and noisy-PAC learning. We prove
several guarantees on the number of tasks labeled by workers for PAC learning
in this setting and show that our algorithm improves upon the baseline by
reducing the total number of tasks given to workers. We demonstrate the
robustness of our algorithm by exploring its application to additional
realistic crowdsourcing settings.Comment: 14 page
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing
We consider a task assignment problem in crowdsourcing, which is aimed at
collecting as many reliable labels as possible within a limited budget. A
challenge in this scenario is how to cope with the diversity of tasks and the
task-dependent reliability of workers, e.g., a worker may be good at
recognizing the name of sports teams, but not be familiar with cosmetics
brands. We refer to this practical setting as heterogeneous crowdsourcing. In
this paper, we propose a contextual bandit formulation for task assignment in
heterogeneous crowdsourcing, which is able to deal with the
exploration-exploitation trade-off in worker selection. We also theoretically
investigate the regret bounds for the proposed method, and demonstrate its
practical usefulness experimentally
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