Artículos de revistas sobre el tema "Stochastic Multi-armed Bandit"
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Xiong, Guojun y Jian Li. "Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 2023): 10528–36. http://dx.doi.org/10.1609/aaai.v37i9.26251.
Texto completoCiucanu, Radu, Pascal Lafourcade, Gael Marcadet y Marta Soare. "SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits". Journal of Artificial Intelligence Research 73 (23 de febrero de 2022): 737–65. http://dx.doi.org/10.1613/jair.1.13163.
Texto completoWan, Zongqi, Zhijie Zhang, Tongyang Li, Jialin Zhang y Xiaoming Sun. "Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junio de 2023): 10087–94. http://dx.doi.org/10.1609/aaai.v37i8.26202.
Texto completoLesage-Landry, Antoine y Joshua A. Taylor. "The Multi-Armed Bandit With Stochastic Plays". IEEE Transactions on Automatic Control 63, n.º 7 (julio de 2018): 2280–86. http://dx.doi.org/10.1109/tac.2017.2765501.
Texto completoEsfandiari, Hossein, Amin Karbasi, Abbas Mehrabian y Vahab Mirrokni. "Regret Bounds for Batched Bandits". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de mayo de 2021): 7340–48. http://dx.doi.org/10.1609/aaai.v35i8.16901.
Texto completoDzhoha, A. S. "Sequential resource allocation in a stochastic environment: an overview and numerical experiments". Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, n.º 3 (2021): 13–25. http://dx.doi.org/10.17721/1812-5409.2021/3.1.
Texto completoJuditsky, A., A. V. Nazin, A. B. Tsybakov y N. Vayatis. "Gap-free Bounds for Stochastic Multi-Armed Bandit". IFAC Proceedings Volumes 41, n.º 2 (2008): 11560–63. http://dx.doi.org/10.3182/20080706-5-kr-1001.01959.
Texto completoAllesiardo, Robin, Raphaël Féraud y Odalric-Ambrym Maillard. "The non-stationary stochastic multi-armed bandit problem". International Journal of Data Science and Analytics 3, n.º 4 (30 de marzo de 2017): 267–83. http://dx.doi.org/10.1007/s41060-017-0050-5.
Texto completoHuo, Xiaoguang y Feng Fu. "Risk-aware multi-armed bandit problem with application to portfolio selection". Royal Society Open Science 4, n.º 11 (noviembre de 2017): 171377. http://dx.doi.org/10.1098/rsos.171377.
Texto completoXu, Lily, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang y Milind Tambe. "Dual-Mandate Patrols: Multi-Armed Bandits for Green Security". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 17 (18 de mayo de 2021): 14974–82. http://dx.doi.org/10.1609/aaai.v35i17.17757.
Texto completoPatil, Vishakha, Ganesh Ghalme, Vineet Nair y Y. Narahari. "Achieving Fairness in the Stochastic Multi-Armed Bandit Problem". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5379–86. http://dx.doi.org/10.1609/aaai.v34i04.5986.
Texto completoJain, Shweta, Satyanath Bhat, Ganesh Ghalme, Divya Padmanabhan y Y. Narahari. "Mechanisms with learning for stochastic multi-armed bandit problems". Indian Journal of Pure and Applied Mathematics 47, n.º 2 (junio de 2016): 229–72. http://dx.doi.org/10.1007/s13226-016-0186-3.
Texto completoCowan, Wesley y Michael N. Katehakis. "MULTI-ARMED BANDITS UNDER GENERAL DEPRECIATION AND COMMITMENT". Probability in the Engineering and Informational Sciences 29, n.º 1 (10 de octubre de 2014): 51–76. http://dx.doi.org/10.1017/s0269964814000217.
Texto completoDunn, R. T. y K. D. Glazebrook. "The performance of index-based policies for bandit problems with stochastic machine availability". Advances in Applied Probability 33, n.º 2 (junio de 2001): 365–90. http://dx.doi.org/10.1017/s0001867800010843.
Texto completoBubeck, Sébastien. "Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems". Foundations and Trends® in Machine Learning 5, n.º 1 (2012): 1–122. http://dx.doi.org/10.1561/2200000024.
Texto completoZayas-Cabán, Gabriel, Stefanus Jasin y Guihua Wang. "An asymptotically optimal heuristic for general nonstationary finite-horizon restless multi-armed, multi-action bandits". Advances in Applied Probability 51, n.º 03 (septiembre de 2019): 745–72. http://dx.doi.org/10.1017/apr.2019.29.
Texto completoRoy Chaudhuri, Arghya y Shivaram Kalyanakrishnan. "Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 06 (3 de abril de 2020): 10085–92. http://dx.doi.org/10.1609/aaai.v34i06.6566.
Texto completoO'Flaherty, Brendan. "Some results on two-armed bandits when both projects vary". Journal of Applied Probability 26, n.º 3 (septiembre de 1989): 655–58. http://dx.doi.org/10.2307/3214424.
Texto completoO'Flaherty, Brendan. "Some results on two-armed bandits when both projects vary". Journal of Applied Probability 26, n.º 03 (septiembre de 1989): 655–58. http://dx.doi.org/10.1017/s0021900200038262.
Texto completoWang, Siwei, Haoyun Wang y Longbo Huang. "Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de mayo de 2021): 10210–17. http://dx.doi.org/10.1609/aaai.v35i11.17224.
Texto completoZuo, Jinhang, Xiaoxi Zhang y Carlee Joe-Wong. "Observe Before Play: Multi-Armed Bandit with Pre-Observations". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 7023–30. http://dx.doi.org/10.1609/aaai.v34i04.6187.
Texto completoNamba, Hiroyuki. "Non-stationary Stochastic Multi-armed Bandit Problems with External Information on Stationarity". Transactions of the Japanese Society for Artificial Intelligence 36, n.º 3 (1 de mayo de 2021): D—K84_1–11. http://dx.doi.org/10.1527/tjsai.36-3_d-k84.
Texto completoAuer, Peter y Ronald Ortner. "UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem". Periodica Mathematica Hungarica 61, n.º 1-2 (septiembre de 2010): 55–65. http://dx.doi.org/10.1007/s10998-010-3055-6.
Texto completoNiño-Mora, José. "Multi-Gear Bandits, Partial Conservation Laws, and Indexability". Mathematics 10, n.º 14 (18 de julio de 2022): 2497. http://dx.doi.org/10.3390/math10142497.
Texto completoOu, Mingdong, Nan Li, Cheng Yang, Shenghuo Zhu y Rong Jin. "Semi-Parametric Sampling for Stochastic Bandits with Many Arms". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 7933–40. http://dx.doi.org/10.1609/aaai.v33i01.33017933.
Texto completoFeldman, Zohar y Carmel Domshlak. "On MABs and Separation of Concerns in Monte-Carlo Planning for MDPs". Proceedings of the International Conference on Automated Planning and Scheduling 24 (10 de mayo de 2014): 120–27. http://dx.doi.org/10.1609/icaps.v24i1.13631.
Texto completoOttens, Brammert, Christos Dimitrakakis y Boi Faltings. "DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems". Proceedings of the AAAI Conference on Artificial Intelligence 26, n.º 1 (20 de septiembre de 2021): 528–34. http://dx.doi.org/10.1609/aaai.v26i1.8129.
Texto completoGuan, Ziwei, Kaiyi Ji, Donald J. Bucci Jr., Timothy Y. Hu, Joseph Palombo, Michael Liston y Yingbin Liang. "Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 4036–43. http://dx.doi.org/10.1609/aaai.v34i04.5821.
Texto completoCowan, Wesley y Michael N. Katehakis. "EXPLORATION–EXPLOITATION POLICIES WITH ALMOST SURE, ARBITRARILY SLOW GROWING ASYMPTOTIC REGRET". Probability in the Engineering and Informational Sciences 34, n.º 3 (26 de enero de 2019): 406–28. http://dx.doi.org/10.1017/s0269964818000529.
Texto completoNiño-Mora, José. "Markovian Restless Bandits and Index Policies: A Review". Mathematics 11, n.º 7 (28 de marzo de 2023): 1639. http://dx.doi.org/10.3390/math11071639.
Texto completoPapagiannis, Tasos, Georgios Alexandridis y Andreas Stafylopatis. "Pruning Stochastic Game Trees Using Neural Networks for Reduced Action Space Approximation". Mathematics 10, n.º 9 (1 de mayo de 2022): 1509. http://dx.doi.org/10.3390/math10091509.
Texto completoGyörgy, A. y L. Kocsis. "Efficient Multi-Start Strategies for Local Search Algorithms". Journal of Artificial Intelligence Research 41 (29 de julio de 2011): 407–44. http://dx.doi.org/10.1613/jair.3313.
Texto completoTrovo, Francesco, Stefano Paladino, Marcello Restelli y Nicola Gatti. "Sliding-Window Thompson Sampling for Non-Stationary Settings". Journal of Artificial Intelligence Research 68 (26 de mayo de 2020): 311–64. http://dx.doi.org/10.1613/jair.1.11407.
Texto completoKillian, Jackson A., Arpita Biswas, Lily Xu, Shresth Verma, Vineet Nair, Aparna Taneja, Aparna Hegde et al. "Robust Planning over Restless Groups: Engagement Interventions for a Large-Scale Maternal Telehealth Program". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junio de 2023): 14295–303. http://dx.doi.org/10.1609/aaai.v37i12.26672.
Texto completoWATANABE, Ryo, Junpei KOMIYAMA, Atsuyoshi NAKAMURA y Mineichi KUDO. "KL-UCB-Based Policy for Budgeted Multi-Armed Bandits with Stochastic Action Costs". IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E100.A, n.º 11 (2017): 2470–86. http://dx.doi.org/10.1587/transfun.e100.a.2470.
Texto completoYoussef, Marie-Josepha, Venugopal V. Veeravalli, Joumana Farah, Charbel Abdel Nour y Catherine Douillard. "Resource Allocation in NOMA-Based Self-Organizing Networks Using Stochastic Multi-Armed Bandits". IEEE Transactions on Communications 69, n.º 9 (septiembre de 2021): 6003–17. http://dx.doi.org/10.1109/tcomm.2021.3092767.
Texto completoSledge, Isaac y José Príncipe. "An Analysis of the Value of Information When Exploring Stochastic, Discrete Multi-Armed Bandits". Entropy 20, n.º 3 (28 de febrero de 2018): 155. http://dx.doi.org/10.3390/e20030155.
Texto completoPokhrel, Shiva Raj y Michel Mandjes. "Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity". Drones 7, n.º 1 (31 de diciembre de 2022): 30. http://dx.doi.org/10.3390/drones7010030.
Texto completoPainter, Michael, Bruno Lacerda y Nick Hawes. "Convex Hull Monte-Carlo Tree-Search". Proceedings of the International Conference on Automated Planning and Scheduling 30 (1 de junio de 2020): 217–25. http://dx.doi.org/10.1609/icaps.v30i1.6664.
Texto completoGasnikov, A. V., E. A. Krymova, A. A. Lagunovskaya, I. N. Usmanova y F. A. Fedorenko. "Stochastic online optimization. Single-point and multi-point non-linear multi-armed bandits. Convex and strongly-convex case". Automation and Remote Control 78, n.º 2 (febrero de 2017): 224–34. http://dx.doi.org/10.1134/s0005117917020035.
Texto completoCiucanu, Radu, Pascal Lafourcade, Marius Lombard-Platet y Marta Soare. "Secure protocols for cumulative reward maximization in stochastic multi-armed bandits". Journal of Computer Security, 2 de febrero de 2022, 1–27. http://dx.doi.org/10.3233/jcs-210051.
Texto completoAmakasu, Takashi, Nicolas Chauvet, Guillaume Bachelier, Serge Huant, Ryoichi Horisaki y Makoto Naruse. "Conflict-free collective stochastic decision making by orbital angular momentum of photons through quantum interference". Scientific Reports 11, n.º 1 (26 de octubre de 2021). http://dx.doi.org/10.1038/s41598-021-00493-2.
Texto completoImmorlica, Nicole, Karthik Abinav Sankararaman, Robert Schapire y Aleksandrs Slivkins. "Adversarial Bandits with Knapsacks". Journal of the ACM, 18 de agosto de 2022. http://dx.doi.org/10.1145/3557045.
Texto completoZhou, Datong y Claire Tomlin. "Budget-Constrained Multi-Armed Bandits With Multiple Plays". Proceedings of the AAAI Conference on Artificial Intelligence 32, n.º 1 (29 de abril de 2018). http://dx.doi.org/10.1609/aaai.v32i1.11629.
Texto completoFernandez-Tapia, Joaquin y Charles Monzani. "Stochastic Multi-Armed Bandit Algorithm for Optimal Budget Allocation in Programmatic Advertising". SSRN Electronic Journal, 2015. http://dx.doi.org/10.2139/ssrn.2600473.
Texto completoGopalan, Aditya, Prashanth L. A., Michael Fu y Steve Marcus. "Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected". Proceedings of the AAAI Conference on Artificial Intelligence 31, n.º 1 (13 de febrero de 2017). http://dx.doi.org/10.1609/aaai.v31i1.10922.
Texto completoMandel, Travis, Yun-En Liu, Emma Brunskill y Zoran Popović. "The Queue Method: Handling Delay, Heuristics, Prior Data, and Evaluation in Bandits". Proceedings of the AAAI Conference on Artificial Intelligence 29, n.º 1 (21 de febrero de 2015). http://dx.doi.org/10.1609/aaai.v29i1.9604.
Texto completoLiu, Fang, Swapna Buccapatnam y Ness Shroff. "Information Directed Sampling for Stochastic Bandits With Graph Feedback". Proceedings of the AAAI Conference on Artificial Intelligence 32, n.º 1 (29 de abril de 2018). http://dx.doi.org/10.1609/aaai.v32i1.11751.
Texto completoHashima, Sherief, Mostafa M. Fouda, Sadman Sakib, Zubair Md Fadlullah, Kohei Hatano, Ehab Mahmoud Mohamed y Xuemin Shen. "Energy-Aware Hybrid RF-VLC Multi-Band Selection in D2D Communication: A Stochastic Multi-Armed Bandit Approach". IEEE Internet of Things Journal, 2022, 1. http://dx.doi.org/10.1109/jiot.2022.3162135.
Texto completoLi, Bo y Chi Ho Yeung. "Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of multi-armed bandits". Chaos: An Interdisciplinary Journal of Nonlinear Science 33, n.º 6 (1 de junio de 2023). http://dx.doi.org/10.1063/5.0120076.
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