24 research outputs found
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization
We address the problem of maximizing an unknown submodular function that can
only be accessed via noisy evaluations. Our work is motivated by the task of
summarizing content, e.g., image collections, by leveraging users' feedback in
form of clicks or ratings. For summarization tasks with the goal of maximizing
coverage and diversity, submodular set functions are a natural choice. When the
underlying submodular function is unknown, users' feedback can provide noisy
evaluations of the function that we seek to maximize. We provide a generic
algorithm -- \submM{} -- for maximizing an unknown submodular function under
cardinality constraints. This algorithm makes use of a novel exploration module
-- \blbox{} -- that proposes good elements based on adaptively sampling noisy
function evaluations. \blbox{} is able to accommodate different kinds of
observation models such as value queries and pairwise comparisons. We provide
PAC-style guarantees on the quality and sampling cost of the solution obtained
by \submM{}. We demonstrate the effectiveness of our approach in an
interactive, crowdsourced image collection summarization application.Comment: Extended version of AAAI'16 pape
Information Gathering in Networks via Active Exploration
How should we gather information in a network, where each node's visibility
is limited to its local neighborhood? This problem arises in numerous
real-world applications, such as surveying and task routing in social networks,
team formation in collaborative networks and experimental design with
dependency constraints. Often the informativeness of a set of nodes can be
quantified via a submodular utility function. Existing approaches for
submodular optimization, however, require that the set of all nodes that can be
selected is known ahead of time, which is often unrealistic. In contrast, we
propose a novel model where we start our exploration from an initial node, and
new nodes become visible and available for selection only once one of their
neighbors has been chosen. We then present a general algorithm NetExp for this
problem, and provide theoretical bounds on its performance dependent on
structural properties of the underlying network. We evaluate our methodology on
various simulated problem instances as well as on data collected from social
question answering system deployed within a large enterprise.Comment: Longer version of IJCAI'15 pape