117 research outputs found
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Submodular maximization is a general optimization problem with a wide range
of applications in machine learning (e.g., active learning, clustering, and
feature selection). In large-scale optimization, the parallel running time of
an algorithm is governed by its adaptivity, which measures the number of
sequential rounds needed if the algorithm can execute polynomially-many
independent oracle queries in parallel. While low adaptivity is ideal, it is
not sufficient for an algorithm to be efficient in practice---there are many
applications of distributed submodular optimization where the number of
function evaluations becomes prohibitively expensive. Motivated by these
applications, we study the adaptivity and query complexity of submodular
maximization. In this paper, we give the first constant-factor approximation
algorithm for maximizing a non-monotone submodular function subject to a
cardinality constraint that runs in adaptive rounds and makes
oracle queries in expectation. In our empirical study, we use
three real-world applications to compare our algorithm with several benchmarks
for non-monotone submodular maximization. The results demonstrate that our
algorithm finds competitive solutions using significantly fewer rounds and
queries.Comment: 12 pages, 8 figure
Scalable Methods for Adaptively Seeding a Social Network
In recent years, social networking platforms have developed into
extraordinary channels for spreading and consuming information. Along with the
rise of such infrastructure, there is continuous progress on techniques for
spreading information effectively through influential users. In many
applications, one is restricted to select influencers from a set of users who
engaged with the topic being promoted, and due to the structure of social
networks, these users often rank low in terms of their influence potential. An
alternative approach one can consider is an adaptive method which selects users
in a manner which targets their influential neighbors. The advantage of such an
approach is that it leverages the friendship paradox in social networks: while
users are often not influential, they often know someone who is.
Despite the various complexities in such optimization problems, we show that
scalable adaptive seeding is achievable. In particular, we develop algorithms
for linear influence models with provable approximation guarantees that can be
gracefully parallelized. To show the effectiveness of our methods we collected
data from various verticals social network users follow. For each vertical, we
collected data on the users who responded to a certain post as well as their
neighbors, and applied our methods on this data. Our experiments show that
adaptive seeding is scalable, and importantly, that it obtains dramatic
improvements over standard approaches of information dissemination.Comment: Full version of the paper appearing in WWW 201
On Connections Between Machine Learning And Information Elicitation, Choice Modeling, And Theoretical Computer Science
Machine learning, which has its origins at the intersection of computer science and statistics, is now a rapidly growing area of research that is being integrated into almost every discipline in science and business such as economics, marketing and information retrieval. As a consequence of this integration, it is necessary to understand how machine learning interacts with these disciplines and to understand fundamental questions that arise at the resulting interfaces. The goal of my thesis research is to study these interdisciplinary questions at the interface of machine learning and other disciplines including mechanism design/information elicitation, preference/choice modeling, and theoretical computer science
Delegated Stochastic Probing
Delegation covers a broad class of problems in which a principal doesn\u27t have the resources or expertise necessary to complete a task by themselves, so they delegate the task to an agent whose interests may not be aligned with their own. Stochastic probing describes problems in which we are tasked with maximizing expected utility by "probing" known distributions for acceptable solutions subject to certain constraints. In this work, we combine the concepts of delegation and stochastic probing into a single mechanism design framework which we term delegated stochastic probing. We study how much a principal loses by delegating a stochastic probing problem, compared to their utility in the non-delegated solution. Our model and results are heavily inspired by the work of Kleinberg and Kleinberg in "Delegated Search Approximates Efficient Search." Building on their work, we show that there exists a connection between delegated stochastic probing and generalized prophet inequalities, which provides us with constant-factor deterministic mechanisms for a large class of delegated stochastic probing problems. We also explore randomized mechanisms in a simple delegated probing setting, and show that they outperform deterministic mechanisms in some instances but not in the worst case
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