A multi-armed bandit approach for exploring partially observed networks

Madhawa, Kaushalya; Murata, Tsuyoshi

doi:10.1007/s41109-019-0145-0

Cited by 8 publications

(10 citation statements)

References 26 publications

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“… 18 , 21 , 65 , 66 In an in principle infinite chemical space, another central AML design choice regards the extent over which new molecules are practically assessed with the established, conceptually global surrogate model. Aiming for high-performance OSC molecules of tractable size and complexity, we here opt for a single tree expansion that limits the candidates to those in the vicinity of already sampled ones 67 .…”

Section: Resultsmentioning

confidence: 99%

Active discovery of organic semiconductors

et al. 2021

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The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.

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Section: Resultsmentioning

confidence: 99%

Active discovery of organic semiconductors

et al. 2021

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“…An important observation is that immediate contacts alone (as done in previous work (e.g., Singla et al 2015, Soundarajan et al 2017, Madhawa and Murata 2019)) may be insufficient to consider for COVID-19 testing and we need a richer representation of similarity between individuals. We seek to model similarity such that if one agent is infected, then a similar node is also possibly infected.…”

Section: Active Sampling Framework and Algorithmmentioning

confidence: 99%

“…Beyond classic bandit problems, there exists literature on active search, with the objective of finding as many target nodes as possible with some given property. Most of the existing work assumes that the complete network structure is known before hand (e.g., Ma et al 2015), with the exception of a few recent papers that consider partially observed networks (e.g., Singla et al 2015, Soundarajan et al 2017, Madhawa and Murata 2019). However these approaches do not consider the node sampling scenario needed for the testing problem considered in this paper.…”

Section: Active Sampling Framework and Algorithmmentioning

confidence: 99%

Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19

Wang

Yahav

Padmanabhan

2021

Preprint

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This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with vaccination and quarantine policies and is adaptive to changes in real-time data. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. The results show that smart-testing is effective in significantly reducing infection and death rates as compared to current policies, with or without vaccination.

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“…Murai et al (2018) propose a multi-armed bandit algorithm for reducing network incompleteness that probabilistically chooses from an ensemble of classifiers that are trained simultaneously. Madhawa and Murata (2019) describe a nonparametric multi-armed bandit based on a k-nearest neighbor upper-confidence bound algorithm, which will be described in more detail in Experiments section.…”

Section: Related Workmentioning

confidence: 99%

Understanding the limitations of network online learning

et al. 2020

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Studies of networked phenomena, such as interactions in online social media, often rely on incomplete data, either because these phenomena are partially observed, or because the data is too large or expensive to acquire all at once. Analysis of incomplete data leads to skewed or misleading results. In this paper, we investigate limitations of learning to complete partially observed networks via node querying. Concretely, we study the following problem: given (i) a partially observed network, (ii) the ability to query nodes for their connections (e.g., by accessing an API), and (iii) a budget on the number of such queries, sequentially learn which nodes to query in order to maximally increase observability. We call this querying process Network Online Learning and present a family of algorithms called NOL*. These algorithms learn to choose which partially observed node to query next based on a parameterized model that is trained online through a process of exploration and exploitation. Extensive experiments on both synthetic and real world networks show that (i) it is possible to sequentially learn to choose which nodes are best to query in a network and (ii) some macroscopic properties of networks, such as the degree distribution and modular structure, impact the potential for learning and the optimal amount of random exploration.

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A multi-armed bandit approach for exploring partially observed networks

Cited by 8 publications

References 26 publications

Active discovery of organic semiconductors

Active discovery of organic semiconductors

Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19

Understanding the limitations of network online learning

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