202,731 research outputs found
Distributed Robust Learning
We propose a framework for distributed robust statistical learning on {\em
big contaminated data}. The Distributed Robust Learning (DRL) framework can
reduce the computational time of traditional robust learning methods by several
orders of magnitude. We analyze the robustness property of DRL, showing that
DRL not only preserves the robustness of the base robust learning method, but
also tolerates contaminations on a constant fraction of results from computing
nodes (node failures). More precisely, even in presence of the most adversarial
outlier distribution over computing nodes, DRL still achieves a breakdown point
of at least , where is the break down point of
corresponding centralized algorithm. This is in stark contrast with naive
division-and-averaging implementation, which may reduce the breakdown point by
a factor of when computing nodes are used. We then specialize the
DRL framework for two concrete cases: distributed robust principal component
analysis and distributed robust regression. We demonstrate the efficiency and
the robustness advantages of DRL through comprehensive simulations and
predicting image tags on a large-scale image set.Comment: 18 pages, 2 figure
ALOHA Random Access that Operates as a Rateless Code
Various applications of wireless Machine-to-Machine (M2M) communications have
rekindled the research interest in random access protocols, suitable to support
a large number of connected devices. Slotted ALOHA and its derivatives
represent a simple solution for distributed random access in wireless networks.
Recently, a framed version of slotted ALOHA gained renewed interest due to the
incorporation of successive interference cancellation (SIC) in the scheme,
which resulted in substantially higher throughputs. Based on similar principles
and inspired by the rateless coding paradigm, a frameless approach for
distributed random access in slotted ALOHA framework is described in this
paper. The proposed approach shares an operational analogy with rateless
coding, expressed both through the user access strategy and the adaptive length
of the contention period, with the objective to end the contention when the
instantaneous throughput is maximized. The paper presents the related analysis,
providing heuristic criteria for terminating the contention period and showing
that very high throughputs can be achieved, even for a low number for
contending users. The demonstrated results potentially have more direct
practical implications compared to the approaches for coded random access that
lead to high throughputs only asymptotically.Comment: Revised version submitted to IEEE Transactions on Communication
Decision and function problems based on boson sampling
Boson sampling is a mathematical problem that is strongly believed to be
intractable for classical computers, whereas passive linear interferometers can
produce samples efficiently. So far, the problem remains a computational
curiosity, and the possible usefulness of boson-sampling devices is mainly
limited to the proof of quantum supremacy. The purpose of this work is to
investigate whether boson sampling can be used as a resource of decision and
function problems that are computationally hard, and may thus have
cryptographic applications. After the definition of a rather general
theoretical framework for the design of such problems, we discuss their
solution by means of a brute-force numerical approach, as well as by means of
non-boson samplers. Moreover, we estimate the sample sizes required for their
solution by passive linear interferometers, and it is shown that they are
independent of the size of the Hilbert space.Comment: Close to the version published in PR
On Counting Triangles through Edge Sampling in Large Dynamic Graphs
Traditional frameworks for dynamic graphs have relied on processing only the
stream of edges added into or deleted from an evolving graph, but not any
additional related information such as the degrees or neighbor lists of nodes
incident to the edges. In this paper, we propose a new edge sampling framework
for big-graph analytics in dynamic graphs which enhances the traditional model
by enabling the use of additional related information. To demonstrate the
advantages of this framework, we present a new sampling algorithm, called Edge
Sample and Discard (ESD). It generates an unbiased estimate of the total number
of triangles, which can be continuously updated in response to both edge
additions and deletions. We provide a comparative analysis of the performance
of ESD against two current state-of-the-art algorithms in terms of accuracy and
complexity. The results of the experiments performed on real graphs show that,
with the help of the neighborhood information of the sampled edges, the
accuracy achieved by our algorithm is substantially better. We also
characterize the impact of properties of the graph on the performance of our
algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2017
Statistical inference framework for source detection of contagion processes on arbitrary network structures
In this paper we introduce a statistical inference framework for estimating
the contagion source from a partially observed contagion spreading process on
an arbitrary network structure. The framework is based on a maximum likelihood
estimation of a partial epidemic realization and involves large scale
simulation of contagion spreading processes from the set of potential source
locations. We present a number of different likelihood estimators that are used
to determine the conditional probabilities associated to observing partial
epidemic realization with particular source location candidates. This
statistical inference framework is also applicable for arbitrary compartment
contagion spreading processes on networks. We compare estimation accuracy of
these approaches in a number of computational experiments performed with the
SIR (susceptible-infected-recovered), SI (susceptible-infected) and ISS
(ignorant-spreading-stifler) contagion spreading models on synthetic and
real-world complex networks
Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View
Distributed adaptive filtering has been considered as an effective approach
for data processing and estimation over distributed networks. Most existing
distributed adaptive filtering algorithms focus on designing different
information diffusion rules, regardless of the nature evolutionary
characteristic of a distributed network. In this paper, we study the adaptive
network from the game theoretic perspective and formulate the distributed
adaptive filtering problem as a graphical evolutionary game. With the proposed
formulation, the nodes in the network are regarded as players and the local
combiner of estimation information from different neighbors is regarded as
different strategies selection. We show that this graphical evolutionary game
framework is very general and can unify the existing adaptive network
algorithms. Based on this framework, as examples, we further propose two
error-aware adaptive filtering algorithms. Moreover, we use graphical
evolutionary game theory to analyze the information diffusion process over the
adaptive networks and evolutionarily stable strategy of the system. Finally,
simulation results are shown to verify the effectiveness of our analysis and
proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin
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