Relevant bibliographies by topics / Explorable uncertainty

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Explorable uncertainty'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Explorable uncertainty"

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Focke, Jacob, Nicole Megow, and Julie Meißner. "Minimum Spanning Tree under Explorable Uncertainty in Theory and Experiments." ACM Journal of Experimental Algorithmics 25 (November 8, 2020): 1–20. http://dx.doi.org/10.1145/3422371.

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Mansour, Yishay, Alex Slivkins, Vasilis Syrgkanis, and Zhiwei Steven Wu. "Bayesian Exploration: Incentivizing Exploration in Bayesian Games." Operations Research 70, no. 2 (2022): 1105–27. http://dx.doi.org/10.1287/opre.2021.2205.

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In a wide range of recommendation systems, self-interested individuals (“agents”) make decisions over time, using information revealed by other agents in the past, and producing information that may help agents in the future. Each agent would like to exploit the best action given the current information but would prefer the previous agents to explore various alternatives to collect information. A social planner, by means of a well-designed recommendation policy, can incentivize the agents to balance exploration and exploitation in order to maximize social welfare or some other objective. The r
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Mazurkevich, Andrey, Ekaterina Dolbunova, and Luca Ottonello. "Archaeological excavations and reconstructions of disappeared archaeological heritage (based on excavations in North-Western Russia)." VITA ANTIQUA 10 (December 20, 2018): 165–75. https://doi.org/10.37098/2519-4542-2018-1-10-165-175.

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Archaeological excavations allow us to investigate archaeological heritage, but at the same time, they lead to its destruction. Multi-layer archaeological sites, which were settled during multiple stages of occupation and include several cultural horizons, represent a number of events. Their “decoding” is possible only by application of various methods – archaeological, natural-scientific, as well as the use of virtual modelling. Archaeological excavations allow tracing of different stages of people inhabitation, whereas digital reconstruction gives the&nb
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Mathwieser, Corinna, and Eranda Çela. "Special cases of the minimum spanning tree problem under explorable edge and vertex uncertainty." Networks, January 11, 2024. http://dx.doi.org/10.1002/net.22204.

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AbstractThis article studies the Minimum Spanning Tree Problem under Explorable Uncertainty as well as a related vertex uncertainty version of the problem. We particularly consider special instance types, including cactus graphs, for which we provide randomized algorithms. We introduce the problem of finding a minimum weight spanning star under uncertainty for which we show that no algorithm can achieve constant competitive ratio.
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Erlebach, Thomas, Michael Hoffmann, and Murilo Santos de Lima. "Round-Competitive Algorithms for Uncertainty Problems with Parallel Queries." Algorithmica, September 15, 2022. http://dx.doi.org/10.1007/s00453-022-01035-6.

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AbstractIn computing with explorable uncertainty, one considers problems where the values of some input elements are uncertain, typically represented as intervals, but can be obtained using queries. Previous work has considered query minimization in the settings where queries are asked sequentially (adaptive model) or all at once (non-adaptive model). We introduce a new model where k queries can be made in parallel in each round, and the goal is to minimize the number of query rounds. Using competitive analysis, we present upper and lower bounds on the number of query rounds required by any al
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Anselmi, Jonatha, and Josu Doncel. "Load Balancing with Job-Size Testing: Performance Improvement or Degradation?" ACM Transactions on Modeling and Performance Evaluation of Computing Systems, March 4, 2024. http://dx.doi.org/10.1145/3651154.

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In the context of decision making under explorable uncertainty, scheduling with testing is a powerful technique used in the management of computer systems to improve performance via better job-dispatching decisions. Upon job arrival, a scheduler may run some testing algorithm against the job to extract some information about its structure, e.g., its size, and properly classify it. The acquisition of such knowledge comes with a cost because the testing algorithm delays the dispatching decisions, though this is under control. In this paper, we analyze the impact of such extra cost in a load bala
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Dissertations / Theses on the topic "Explorable uncertainty"

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Dogeas, Konstantinos. "Energy Minimization, Data Movement and Uncertainty : Models and Algorithms." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS070.pdf.

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Les plateformes de calcul haute performance (HPC) sont la solution idéale pour exécuter des applications exigeantes en termes de calcul. Étant donné leur consommation importante en énergie, le besoin d'algorithmes plus efficaces en termes d'énergie est indispensable. De meilleurs algorithmes d'ordonnancement peuvent être conçus en exploitant les caractéristiques essentielles d'une plateforme HPC, telles que sa topologie de réseau et l'hétérogénéité de ses machines. On peut également obtenir de meilleures performances en concevant des modèles plus réalistes, qui saisissent les fonctionnalités d
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Book chapters on the topic "Explorable uncertainty"

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Evans, William, and Seyed Ali Tabatabaee. "Perpetual Scheduling with Explorable Uncertainty." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92935-9_21.

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Megow, Nicole, and Jens Schlöter. "Explorable Uncertainty Meets Decision-Making in Logistics." In Dynamics in Logistics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_2.

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AbstractDecision-making under uncertainty is a major challenge in logistics. Mathematical optimization has a long tradition in providing powerful methods for solving logistics problems. While classical optimization models for uncertainty in the input data do not consider the option to actively query the precise value of uncertain input elements, this option is in practice often available at a certain cost. The recent line of research on optimization under explorable uncertainty develops methods with provable performance guarantees for such scenarios. In this chapter, we highlight some recent r
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Erlebach, Thomas. "Computing and Scheduling with Explorable Uncertainty." In Sailing Routes in the World of Computation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94418-0_16.

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Liu, Alison Hsiang-Hsuan, Fu-Hong Liu, Prudence W. H. Wong, and Xiao-Ou Zhang. "The Power of Amortization on Scheduling with Explorable Uncertainty." In Approximation and Online Algorithms. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49815-2_7.

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Megow, Nicole, and Jens Schlöter. "Set Selection Under Explorable Stochastic Uncertainty via Covering Techniques." In Integer Programming and Combinatorial Optimization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32726-1_23.

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Albers, Susanne, and Alexander Eckl. "Explorable Uncertainty in Scheduling with Non-uniform Testing Times." In Approximation and Online Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2_9.

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AbstractThe problem of scheduling with testing in the framework of explorable uncertainty models environments where some preliminary action can influence the duration of a task. In the model, each job has an unknown processing time that can be revealed by running a test. Alternatively, jobs may be run untested for the duration of a given upper limit. Recently, Dürr et al. [4] have studied the setting where all testing times are of unit size and have given lower and upper bounds for the objectives of minimizing the sum of completion times and the makespan on a single machine. In this paper, we
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Conference papers on the topic "Explorable uncertainty"

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Bampis, Evripidis, Konstantinos Dogeas, Alexander Kononov, Giorgio Lucarelli, and Fanny Pascual. "Speed Scaling with Explorable Uncertainty." In SPAA '21: 33rd ACM Symposium on Parallelism in Algorithms and Architectures. ACM, 2021. http://dx.doi.org/10.1145/3409964.3461812.

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Erlebach, Thomas, Murilo de Lima, Nicole Megow, and Jens Schlöter. "Sorting and Hypergraph Orientation under Uncertainty with Predictions." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/619.

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Learning-augmented algorithms have been attracting increasing interest, but have only recently been considered in the setting of explorable uncertainty where precise values of uncertain input elements can be obtained by a query and the goal is to minimize the number of queries needed to solve a problem. We study learning-augmented algorithms for sorting and hypergraph orientation under uncertainty, assuming access to untrusted predictions for the uncertain values. Our algorithms provide improved performance guarantees for accurate predictions while maintaining worst-case guarantees that are be
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Mauricio, Cristóbal Alfredo, Sebastian Davila-Gálvez, and Óscar Carlos Vasquez. "When a test-taking strategy is better? An approach from the paradigm of scheduling under explorable uncertainty." In Ninth International Conference on Higher Education Advances. Universitat Politècnica de València, 2023. http://dx.doi.org/10.4995/head23.2023.16371.

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In this article, we adopt the paradigm of scheduling under explorable uncertainty to explore test-taking strategies to solve standardized tests in terms of maximizing the correct questions answered. From this approach, a test taker considers a number of questions and has the possibility to read in order to obtain the difficulty of the question. Later, he/she has the option, for example, to answer the question or to skip the one that seemed difficult and read the next question in the test. Specifically, we state the problem definition by considering two test-taking strategies, formulate and imp
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