The Experts below are selected from a list of 38958 Experts worldwide ranked by ideXlab platform
Aarti Singh - One of the best experts on this subject based on the ideXlab platform.
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low rank matrix and tensor completion via Adaptive Sampling
Neural Information Processing Systems, 2013Co-Authors: Akshay Krishnamurthy, Aarti SinghAbstract:We study low rank matrix and tensor completion and propose novel algorithms that employ Adaptive Sampling schemes to obtain strong performance guarantees. Our algorithms exploit adaptivity to identify entries that are highly informative for learning the column space of the matrix (tensor) and consequently, our results hold even when the row space is highly coherent, in contrast with previous analyses. In the absence of noise, we show that one can exactly recover a n x n matrix of rank r from merely Ω(nr3/2 log(r)) matrix entries. We also show that one can recover an order T tensor using Ω(nrT-1/2T2 log(r)) entries. For noisy recovery, our algorithm consistently estimates a low rank matrix corrupted with noise using Ω(nr3/2polylog(n)) entries. We complement our study with simulations that verify our theory and demonstrate the scalability of our algorithms.
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low rank matrix and tensor completion via Adaptive Sampling
arXiv: Machine Learning, 2013Co-Authors: Akshay Krishnamurthy, Aarti SinghAbstract:We study low rank matrix and tensor completion and propose novel algorithms that employ Adaptive Sampling schemes to obtain strong performance guarantees. Our algorithms exploit adaptivity to identify entries that are highly informative for learning the column space of the matrix (tensor) and consequently, our results hold even when the row space is highly coherent, in contrast with previous analyses. In the absence of noise, we show that one can exactly recover a $n \times n$ matrix of rank $r$ from merely $\Omega(n r^{3/2}\log(r))$ matrix entries. We also show that one can recover an order $T$ tensor using $\Omega(n r^{T-1/2}T^2 \log(r))$ entries. For noisy recovery, our algorithm consistently estimates a low rank matrix corrupted with noise using $\Omega(n r^{3/2} \textrm{polylog}(n))$ entries. We complement our study with simulations that verify our theory and demonstrate the scalability of our algorithms.
Katia Sycara - One of the best experts on this subject based on the ideXlab platform.
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distributed environmental modeling and Adaptive Sampling for multi robot sensor coverage
Adaptive Agents and Multi-Agents Systems, 2019Co-Authors: Wenhao Luo, Changjoo Nam, George Kantor, Katia SycaraAbstract:We consider the problem of online distributed environmental modeling and Adaptive Sampling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the sensing performance over environmental phenomena, whose distribution is often referred to as a density function. Unlike most existing works that either assume certain knowledge of the density function beforehand or centrally learn the density function assuming global knowledge of collected data from all the robots, we propose a fully distributed Adaptive Sampling approach to allow robots to efficiently learn the unknown density function online. In particular, we developed Adaptive coverage controllers based on the learned density functions for minimizing the sensing cost. To capture significantly different components of the environmental phenomenon with only locally collected data for each robot when global knowledge is not available, we propose a distributed mixture of Gaussian Processes algorithm that enables robots to collaboratively learn the global density function by exchanging only model-related parameters. We empirically demonstrate the effectiveness of our algorithm via evaluation on real-world data gathered from agricultural field robot and indoor static sensors.
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Adaptive Sampling and online learning in multi robot sensor coverage with mixture of gaussian processes
International Conference on Robotics and Automation, 2018Co-Authors: Wenhao Luo, Katia SycaraAbstract:We consider the problem of online environmental Sampling and modeling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the overall sensing performance. In contrast to most existing works on multi-robot coverage control that assume prior knowledge of the distribution of environmental phenomenon, also known as density function, we relax this assumption and enable the robot team to efficiently learn the model of the unknown density function Online using Adaptive Sampling and non-parametric inference such as Gaussian Process (GP). To capture significantly different components of the environmental phenomenon, we propose a new approach with mixture of locally learned Gaussian Processes for collective model learning and an information-theoretic criterion for simultaneous Adaptive Sampling in multi-robot coverage. Our approach demonstrates a better generalization of the environment modeling and thus the improved performance of coverage without assuming the density function is known a priori. We demonstrate the effectiveness of our algorithm via simulations of information gathering from indoor static sensors.
Akshay Krishnamurthy - One of the best experts on this subject based on the ideXlab platform.
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low rank matrix and tensor completion via Adaptive Sampling
Neural Information Processing Systems, 2013Co-Authors: Akshay Krishnamurthy, Aarti SinghAbstract:We study low rank matrix and tensor completion and propose novel algorithms that employ Adaptive Sampling schemes to obtain strong performance guarantees. Our algorithms exploit adaptivity to identify entries that are highly informative for learning the column space of the matrix (tensor) and consequently, our results hold even when the row space is highly coherent, in contrast with previous analyses. In the absence of noise, we show that one can exactly recover a n x n matrix of rank r from merely Ω(nr3/2 log(r)) matrix entries. We also show that one can recover an order T tensor using Ω(nrT-1/2T2 log(r)) entries. For noisy recovery, our algorithm consistently estimates a low rank matrix corrupted with noise using Ω(nr3/2polylog(n)) entries. We complement our study with simulations that verify our theory and demonstrate the scalability of our algorithms.
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low rank matrix and tensor completion via Adaptive Sampling
arXiv: Machine Learning, 2013Co-Authors: Akshay Krishnamurthy, Aarti SinghAbstract:We study low rank matrix and tensor completion and propose novel algorithms that employ Adaptive Sampling schemes to obtain strong performance guarantees. Our algorithms exploit adaptivity to identify entries that are highly informative for learning the column space of the matrix (tensor) and consequently, our results hold even when the row space is highly coherent, in contrast with previous analyses. In the absence of noise, we show that one can exactly recover a $n \times n$ matrix of rank $r$ from merely $\Omega(n r^{3/2}\log(r))$ matrix entries. We also show that one can recover an order $T$ tensor using $\Omega(n r^{T-1/2}T^2 \log(r))$ entries. For noisy recovery, our algorithm consistently estimates a low rank matrix corrupted with noise using $\Omega(n r^{3/2} \textrm{polylog}(n))$ entries. We complement our study with simulations that verify our theory and demonstrate the scalability of our algorithms.
Wenhao Luo - One of the best experts on this subject based on the ideXlab platform.
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distributed environmental modeling and Adaptive Sampling for multi robot sensor coverage
Adaptive Agents and Multi-Agents Systems, 2019Co-Authors: Wenhao Luo, Changjoo Nam, George Kantor, Katia SycaraAbstract:We consider the problem of online distributed environmental modeling and Adaptive Sampling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the sensing performance over environmental phenomena, whose distribution is often referred to as a density function. Unlike most existing works that either assume certain knowledge of the density function beforehand or centrally learn the density function assuming global knowledge of collected data from all the robots, we propose a fully distributed Adaptive Sampling approach to allow robots to efficiently learn the unknown density function online. In particular, we developed Adaptive coverage controllers based on the learned density functions for minimizing the sensing cost. To capture significantly different components of the environmental phenomenon with only locally collected data for each robot when global knowledge is not available, we propose a distributed mixture of Gaussian Processes algorithm that enables robots to collaboratively learn the global density function by exchanging only model-related parameters. We empirically demonstrate the effectiveness of our algorithm via evaluation on real-world data gathered from agricultural field robot and indoor static sensors.
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Adaptive Sampling and online learning in multi robot sensor coverage with mixture of gaussian processes
International Conference on Robotics and Automation, 2018Co-Authors: Wenhao Luo, Katia SycaraAbstract:We consider the problem of online environmental Sampling and modeling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the overall sensing performance. In contrast to most existing works on multi-robot coverage control that assume prior knowledge of the distribution of environmental phenomenon, also known as density function, we relax this assumption and enable the robot team to efficiently learn the model of the unknown density function Online using Adaptive Sampling and non-parametric inference such as Gaussian Process (GP). To capture significantly different components of the environmental phenomenon, we propose a new approach with mixture of locally learned Gaussian Processes for collective model learning and an information-theoretic criterion for simultaneous Adaptive Sampling in multi-robot coverage. Our approach demonstrates a better generalization of the environment modeling and thus the improved performance of coverage without assuming the density function is known a priori. We demonstrate the effectiveness of our algorithm via simulations of information gathering from indoor static sensors.
Gaurav S. Sukhatme - One of the best experts on this subject based on the ideXlab platform.
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Multi-robot coordination through dynamic Voronoi partitioning for informative Adaptive Sampling in communication-constrained environments
Proceedings - IEEE International Conference on Robotics and Automation, 2017Co-Authors: Stephanie Kemna, Carlos Nieto-granda, John G Rogers, Stuart Young, Gaurav S. SukhatmeAbstract:— Autonomous underwater vehicles (AUVs) are cost-and time-efficient systems for environmental Sampling. Informa-tive Adaptive Sampling has been shown to be an effective method of Sampling a lake or ocean for environmental modeling. In this paper, we focus on multi-robot coordination for informative Adaptive Sampling. We use a dynamic Voronoi partitioning approach whereby the vehicles, in a decentralized fashion, repeatedly calculate weighted Voronoi partitions for the space. Each vehicle then runs informative Adaptive Sampling within their partition. The vehicles can request surfacing events to share data between vehicles. Simulation results show that the addition of the coordination with dynamic Voronoi partitioning results in obtaining higher quality models faster. Thus we created a decentralized, multi-robot coordination approach for informative, Adaptive Sampling of unknown environments.
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Adaptive Sampling for estimating a scalar field using a robotic boat and a sensor network
International Conference on Robotics and Automation, 2007Co-Authors: Bin Zhang, Gaurav S. SukhatmeAbstract:This paper introduces an Adaptive Sampling algorithm for a mobile sensor network to estimate a scalar field. The sensor network consists of static nodes and one mobile robot. The static nodes are able to take sensor readings continuously in place, while the mobile robot is able to move and sample at multiple locations. The measurements from the robot and the static nodes are used to reconstruct an underlying scalar field. The algorithm presented in this paper accepts the measurements made by the static nodes as inputs and computes a path for the mobile robot which minimizes the integrated mean square error of the reconstructed field subject to the constraint that the robot has limited energy. We assume that the field does not change when robot is taking samples. In addition to simulations, we have validated the algorithm on a robotic boat and a system of static buoys operating in a lake over several km of traversed distance while reconstructing the temperature field of the lake surface
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aqu1 Adaptive Sampling for marine microorganism monitoring escholarship
2005Co-Authors: Bin Zhang, A A G Requicha, Amit Dhariwal, Eric Shieh, Beth Stauffer, Carl Oberg, Dave Caron, Gaurav S. SukhatmeAbstract:The goal of Adaptive Sampling is to acquire high-resolution data in the important regions with relative less sensor nodes. We built a system to study the behavior of algae. The system consists of both static sensor nodes and mobile sensor nodes. The sensors equipped include thermistors and fluorometers. Thermistors measure one of key environment parameters for the growth of the algae while the fluorescence of chlorophyll provides useful pro xy for algal abundance. The system has been deployed in Lake Fulmor in James Reserve and the data collected in field are presented.