3,640 research outputs found
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs
This paper presents a novel decentralized high-dimensional Bayesian
optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms,
can exploit the interdependent effects of various input components on the
output of the unknown objective function f for boosting the BO performance and
still preserve scalability in the number of input dimensions without requiring
prior knowledge or the existence of a low (effective) dimension of the input
space. To realize this, we propose a sparse yet rich factor graph
representation of f to be exploited for designing an acquisition function that
can be similarly represented by a sparse factor graph and hence be efficiently
optimized in a decentralized manner using distributed message passing. Despite
richly characterizing the interdependent effects of the input components on the
output of f with a factor graph, DEC-HBO can still guarantee no-regret
performance asymptotically. Empirical evaluation on synthetic and real-world
experiments (e.g., sparse Gaussian process model with 1811 hyperparameters)
shows that DEC-HBO outperforms the state-of-the-art HBO algorithms.Comment: 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), Extended
version with proofs, 13 page
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception
This paper presents novel Gaussian process decentralized data fusion
algorithms exploiting the notion of agent-centric support sets for distributed
cooperative perception of large-scale environmental phenomena. To overcome the
limitations of scale in existing works, our proposed algorithms allow every
mobile sensing agent to choose a different support set and dynamically switch
to another during execution for encapsulating its own data into a local summary
that, perhaps surprisingly, can still be assimilated with the other agents'
local summaries (i.e., based on their current choices of support sets) into a
globally consistent summary to be used for predicting the phenomenon. To
achieve this, we propose a novel transfer learning mechanism for a team of
agents capable of sharing and transferring information encapsulated in a
summary based on a support set to that utilizing a different support set with
some loss that can be theoretically bounded and analyzed. To alleviate the
issue of information loss accumulating over multiple instances of transfer
learning, we propose a new information sharing mechanism to be incorporated
into our algorithms in order to achieve memory-efficient lazy transfer
learning. Empirical evaluation on real-world datasets show that our algorithms
outperform the state-of-the-art methods.Comment: 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), Extended
version with proofs, 14 page
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization
Bayesian Optimization (BO) is typically used to optimize an unknown function
that is noisy and costly to evaluate, by exploiting an acquisition function
that must be maximized at each optimization step. Even if provably
asymptotically optimal BO algorithms are efficient at optimizing
low-dimensional functions, scaling them to high-dimensional spaces remains an
open problem, often tackled by assuming an additive structure for . By doing
so, BO algorithms typically introduce additional restrictive assumptions on the
additive structure that reduce their applicability domain. This paper contains
two main contributions: (i) we relax the restrictive assumptions on the
additive structure of without weakening the maximization guarantees of the
acquisition function, and (ii) we address the over-exploration problem for
decentralized BO algorithms. To these ends, we propose DuMBO, an asymptotically
optimal decentralized BO algorithm that achieves very competitive performance
against state-of-the-art BO algorithms, especially when the additive structure
of comprises high-dimensional factors
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