829 research outputs found
Unimodal Thompson Sampling for Graph-Structured Arms
We study, to the best of our knowledge, the first Bayesian algorithm for
unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this
setting, each arm corresponds to a node of a graph and each edge provides a
relationship, unknown to the learner, between two nodes in terms of expected
reward. Furthermore, for any node of the graph there is a path leading to the
unique node providing the maximum expected reward, along which the expected
reward is monotonically increasing. Previous results on this setting describe
the behavior of frequentist MAB algorithms. In our paper, we design a Thompson
Sampling-based algorithm whose asymptotic pseudo-regret matches the lower bound
for the considered setting. We show that -as it happens in a wide number of
scenarios- Bayesian MAB algorithms dramatically outperform frequentist ones. In
particular, we provide a thorough experimental evaluation of the performance of
our and state-of-the-art algorithms as the properties of the graph vary
Seeding with Costly Network Information
We study the task of selecting nodes in a social network of size , to
seed a diffusion with maximum expected spread size, under the independent
cascade model with cascade probability . Most of the previous work on this
problem (known as influence maximization) focuses on efficient algorithms to
approximate the optimal seed set with provable guarantees, given the knowledge
of the entire network. However, in practice, obtaining full knowledge of the
network is very costly. To address this gap, we first study the achievable
guarantees using influence samples. We provide an approximation
algorithm with a tight (1-1/e){\mbox{OPT}}-\epsilon n guarantee, using
influence samples and show that this dependence on
is asymptotically optimal. We then propose a probing algorithm that queries
edges from the graph and use them to find a seed set with the
same almost tight approximation guarantee. We also provide a matching (up to
logarithmic factors) lower-bound on the required number of edges. To address
the dependence of our probing algorithm on the independent cascade probability
, we show that it is impossible to maintain the same approximation
guarantees by controlling the discrepancy between the probing and seeding
cascade probabilities. Instead, we propose to down-sample the probed edges to
match the seeding cascade probability, provided that it does not exceed that of
probing. Finally, we test our algorithms on real world data to quantify the
trade-off between the cost of obtaining more refined network information and
the benefit of the added information for guiding improved seeding strategies
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
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization
Protecting vast quantities of data poses a daunting challenge for the growing
number of organizations that collect, stockpile, and monetize it. The ability
to distinguish data that is actually needed from data collected "just in case"
would help these organizations to limit the latter's exposure to attack. A
natural approach might be to monitor data use and retain only the working-set
of in-use data in accessible storage; unused data can be evicted to a highly
protected store. However, many of today's big data applications rely on machine
learning (ML) workloads that are periodically retrained by accessing, and thus
exposing to attack, the entire data store. Training set minimization methods,
such as count featurization, are often used to limit the data needed to train
ML workloads to improve performance or scalability. We present Pyramid, a
limited-exposure data management system that builds upon count featurization to
enhance data protection. As such, Pyramid uniquely introduces both the idea and
proof-of-concept for leveraging training set minimization methods to instill
rigor and selectivity into big data management. We integrated Pyramid into
Spark Velox, a framework for ML-based targeting and personalization. We
evaluate it on three applications and show that Pyramid approaches
state-of-the-art models while training on less than 1% of the raw data
- …