1,909 research outputs found
Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity
In bipartite matching problems, vertices on one side of a bipartite graph are
paired with those on the other. In its online variant, one side of the graph is
available offline, while the vertices on the other side arrive online. When a
vertex arrives, an irrevocable and immediate decision should be made by the
algorithm; either match it to an available vertex or drop it. Examples of such
problems include matching workers to firms, advertisers to keywords, organs to
patients, and so on. Much of the literature focuses on maximizing the total
relevance---modeled via total weight---of the matching. However, in many
real-world problems, it is also important to consider contributions of
diversity: hiring a diverse pool of candidates, displaying a relevant but
diverse set of ads, and so on. In this paper, we propose the Online Submodular
Bipartite Matching (\osbm) problem, where the goal is to maximize a submodular
function over the set of matched edges. This objective is general enough to
capture the notion of both diversity (\emph{e.g.,} a weighted coverage
function) and relevance (\emph{e.g.,} the traditional linear function)---as
well as many other natural objective functions occurring in practice
(\emph{e.g.,} limited total budget in advertising settings). We propose novel
algorithms that have provable guarantees and are essentially optimal when
restricted to various special cases. We also run experiments on real-world and
synthetic datasets to validate our algorithms.Comment: To appear in AAAI 201
Finish Them!: Pricing Algorithms for Human Computation
Given a batch of human computation tasks, a commonly ignored aspect is how
the price (i.e., the reward paid to human workers) of these tasks must be set
or varied in order to meet latency or cost constraints. Often, the price is set
up-front and not modified, leading to either a much higher monetary cost than
needed (if the price is set too high), or to a much larger latency than
expected (if the price is set too low). Leveraging a pricing model from prior
work, we develop algorithms to optimally set and then vary price over time in
order to meet a (a) user-specified deadline while minimizing total monetary
cost (b) user-specified monetary budget constraint while minimizing total
elapsed time. We leverage techniques from decision theory (specifically, Markov
Decision Processes) for both these problems, and demonstrate that our
techniques lead to upto 30\% reduction in cost over schemes proposed in prior
work. Furthermore, we develop techniques to speed-up the computation, enabling
users to leverage the price setting algorithms on-the-fly
Blind Multiclass Ensemble Classification
The rising interest in pattern recognition and data analytics has spurred the
development of innovative machine learning algorithms and tools. However, as
each algorithm has its strengths and limitations, one is motivated to
judiciously fuse multiple algorithms in order to find the "best" performing
one, for a given dataset. Ensemble learning aims at such high-performance
meta-algorithm, by combining the outputs from multiple algorithms. The present
work introduces a blind scheme for learning from ensembles of classifiers,
using a moment matching method that leverages joint tensor and matrix
factorization. Blind refers to the combiner who has no knowledge of the
ground-truth labels that each classifier has been trained on. A rigorous
performance analysis is derived and the proposed scheme is evaluated on
synthetic and real datasets.Comment: To appear in IEEE Transactions in Signal Processin
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