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Статті в журналах з теми "Upper Confidence Bound"
Francisco-Valencia, Iván, José Raymundo Marcial-Romero, and Rosa María Valdovinos-Rosas. "Upper Confidence Bound o Upper Cofidence Bound Tuned para General Game Playing: Un estudio empírico." Research in Computing Science 147, no. 8 (December 31, 2018): 301–9. http://dx.doi.org/10.13053/rcs-147-8-23.
Повний текст джерелаSaffidine, Abdallah, Tristan Cazenave, and Jean Méhat. "UCD : Upper confidence bound for rooted directed acyclic graphs." Knowledge-Based Systems 34 (October 2012): 26–33. http://dx.doi.org/10.1016/j.knosys.2011.11.014.
Повний текст джерелаBollabás, Béla, and Alan Stacey. "Approximate upper bounds for the critical probability of oriented percolation in two dimensions based on rapidly mixing Markov chains." Journal of Applied Probability 34, no. 4 (December 1997): 859–67. http://dx.doi.org/10.2307/3215002.
Повний текст джерелаBollabás, Béla, and Alan Stacey. "Approximate upper bounds for the critical probability of oriented percolation in two dimensions based on rapidly mixing Markov chains." Journal of Applied Probability 34, no. 04 (December 1997): 859–67. http://dx.doi.org/10.1017/s0021900200101573.
Повний текст джерелаCruse, Thomas A., and Jeffrey M. Brown. "Confidence Interval Simulation for Systems of Random Variables." Journal of Engineering for Gas Turbines and Power 129, no. 3 (October 11, 2005): 836–42. http://dx.doi.org/10.1115/1.2718217.
Повний текст джерелаOttens, Brammert, Christos Dimitrakakis, and Boi Faltings. "DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 528–34. http://dx.doi.org/10.1609/aaai.v26i1.8129.
Повний текст джерелаRadović, Nevena, and Milena Erceg. "Hardware implementation of the upper confidence-bound algorithm for reinforcement learning." Computers & Electrical Engineering 96 (December 2021): 107537. http://dx.doi.org/10.1016/j.compeleceng.2021.107537.
Повний текст джерелаMelesko, Jaroslav, and Vitalij Novickij. "Computer Adaptive Testing Using Upper-Confidence Bound Algorithm for Formative Assessment." Applied Sciences 9, no. 20 (October 14, 2019): 4303. http://dx.doi.org/10.3390/app9204303.
Повний текст джерелаDzhoha, Andrii, and Iryna Rozora. "Beta Upper Confidence Bound Policy for the Design of Clinical Trials." Austrian Journal of Statistics 52, SI (August 15, 2023): 26–39. http://dx.doi.org/10.17713/ajs.v52isi.1751.
Повний текст джерелаWALLINGA, J., D. LÉVY-BRUHL, N. J. GAY, and C. H. WACHMANN. "Estimation of measles reproduction ratios and prospects for elimination of measles by vaccination in some Western European countries." Epidemiology and Infection 127, no. 2 (October 2001): 281–95. http://dx.doi.org/10.1017/s095026880100601x.
Повний текст джерелаДисертації з теми "Upper Confidence Bound"
Modi, Navikkumar. "Machine Learning and Statistical Decision Making for Green Radio." Thesis, CentraleSupélec, 2017. http://www.theses.fr/2017SUPL0002/document.
Повний текст джерелаFuture cellular network technologies are targeted at delivering self-organizable and ultra-high capacity networks, while reducing their energy consumption. This thesis studies intelligent spectrum and topology management through cognitive radio techniques to improve the capacity density and Quality of Service (QoS) as well as to reduce the cooperation overhead and energy consumption. This thesis investigates how reinforcement learning can be used to improve the performance of a cognitive radio system. In this dissertation, we deal with the problem of opportunistic spectrum access in infrastructureless cognitive networks. We assume that there is no information exchange between users, and they have no knowledge of channel statistics and other user's actions. This particular problem is designed as multi-user restless Markov multi-armed bandit framework, in which multiple users collect a priori unknown reward by selecting a channel. The main contribution of the dissertation is to propose a learning policy for distributed users, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB (RQoS-UCB), achieves at most logarithmic order regret. Moreover, numerical studies show that the performance of the cognitive radio system can be significantly enhanced by utilizing proposed learning policies since the cognitive devices are able to identify the appropriate resources more efficiently. This dissertation also introduces a reinforcement learning and transfer learning frameworks to improve the energy efficiency (EE) of the heterogeneous cellular network. Specifically, we formulate and solve an energy efficiency maximization problem pertaining to dynamic base stations (BS) switching operation, which is identified as a combinatorial learning problem, with restless Markov multi-armed bandit framework. Furthermore, a dynamic topology management using the previously defined algorithm, RQoS-UCB, is introduced to intelligently control the working modes of BSs, based on traffic load and capacity in multiple cells. Moreover, to cope with initial reward loss and to speed up the learning process, a transfer RQoS-UCB policy, which benefits from the transferred knowledge observed in historical periods, is proposed and provably converges. Then, proposed dynamic BS switching operation is demonstrated to reduce the number of activated BSs while maintaining an adequate QoS. Extensive numerical simulations demonstrate that the transfer learning significantly reduces the QoS fluctuation during traffic variation, and it also contributes to a performance jump-start and presents significant EE improvement under various practical traffic load profiles. Finally, a proof-of-concept is developed to verify the performance of proposed learning policies on a real radio environment and real measurement database of HF band. Results show that proposed multi-armed bandit learning policies using dual criterion (e.g. availability and quality) optimization for opportunistic spectrum access is not only superior in terms of spectrum utilization but also energy efficient
Hadiji, Hédi. "On some adaptivity questions in stochastic multi-armed bandits." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM021.
Повний текст джерелаThe main topics adressed in this thesis lie in the general domain of sequential learning, and in particular stochastic multi-armed bandits. The thesis is divided into four chapters and an introduction. In the first part of the main body of the thesis, we design a new algorithm achieving, simultaneously, distribution-dependent and distribution-free optimal guarantees. The next two chapters are devoted to adaptivity questions. First, in the context of continuum-armed bandits, we present a new algorithm which, for the first time, does not require the knowledge of the regularity of the bandit problem it is facing. Then, we study the issue of adapting to the unknown support of the payoffs in bounded K-armed bandits. We provide a procedure that (almost) obtains the same guarantees as if it was given the support in advance. In the final chapter, we study a slightly different bandit setting, designed to enforce diversity-preserving conditions on the strategies. We show that the optimal regert in this setting at a speed that is quite different from the traditional bandit setting. In particular, we observe that bounded regret is possible under some specific hypotheses
Iacob, Alexandra. "Scalable Model-Free Algorithms for Influencer Marketing." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG012.
Повний текст джерелаMotivated by scenarios of information diffusion and advertising in social media, we study an emph{influence maximization} (IM) problem in which little is assumed to be known about the diffusion network or about the model that determines how information may propagate. In such a highly uncertain environment, one can focus on emph{multi-round diffusion campaigns}, with the objective to maximize the number of distinct users that are influenced or activated, starting from a known base of few influential nodes.During a campaign, spread seeds are selected sequentially at consecutive rounds, and feedback is collected in the form of the activated nodes at each round.A round's impact (reward) is then quantified as the number of emph{newly activated nodes}.Overall, one must maximize the campaign's total spread, as the sum of rounds' rewards.We consider two sub-classes of IM, emph{cimp} (CIMP) and emph{ecimp} (ECIMP), where (i) the reward of a given round of an ongoing campaign consists of only the extit{new activations} (not observed at previous rounds within that campaign), (ii) the round's context and the historical data from previous rounds can be exploited to learn the best policy, and (iii) ECIMP is CIMP repeated multiple times, offering the possibility of learning from previous campaigns as well.This problem is directly motivated by the real-world scenarios of information diffusion in emph{influencer marketing}, where (i) only a target user's emph{first} / unique activation is of interest (and this activation will emph{persist} as an acquired, latent one throughout the campaign), and (ii) valuable side-information is available to the learning agent.In this setting, an explore-exploit approach could be used to learn the key underlying diffusion parameters, while running the campaigns.For CIMP, we describe and compare two methods of emph{contextual multi-armed bandits}, with emph{upper-confidence bounds} on the remaining potential of influencers, one using a generalized linear model and the Good-Turing estimator for remaining potential (glmucb), and another one that directly adapts the LinUCB algorithm to our setting (linucb).For ECIMP, we propose the algorithmlgtlsvi, which implements the extit{optimism in the face of uncertainty} principle for episodic reinforcement learning with linear approximation. The learning agent estimates for each seed node its remaining potential with a Good-Turing estimator, modified by an estimated Q-function.We show that they outperform baseline methods using state-of-the-art ideas, on synthetic and real-world data, while at the same time exhibiting different and complementary behavior, depending on the scenarios in which they are deployed
Piette, Eric. "Une nouvelle approche au General Game Playing dirigée par les contraintes." Thesis, Artois, 2016. http://www.theses.fr/2016ARTO0401/document.
Повний текст джерелаThe ability for a computer program to effectively play any strategic game, often referred to General Game Playing (GGP), is a key challenge in AI. The GGP competitions, where any game is represented according to a set of logical rules in the Game Description Language (GDL), have led researches to compare various approaches, including Monte Carlo methods, automatic constructions of evaluation functions, logic programming, and answer set programming through some general game players. In this thesis, we offer a new approach driven by stochastic constraints. We first focus on a translation process from GDL to stochastic constraint networks (SCSP) in order to provide compact representations of strategic games and to model strategies. In a second part, we exploit a fragment of SCSP through an algorithm called MAC-UCB by coupling the MAC (Maintaining Arc Consistency) algorithm, used to solve each stage of the SCSP in turn, together with the UCB (Upper Confidence Bound) policy for approximating the values of those strategies obtained by the last stage in the sequence. The efficiency of this technical on the others GGP approaches is confirmed by WoodStock, implementing MAC-UCB, the actual leader on the GGP Continuous Tournament. Finally, in the last part, we propose an alternative approach to symmetry detection in stochastic games, inspired from constraint programming techniques. We demonstrate experimentally that MAC-UCB, coupled with our constranit-based symmetry detection approach, significantly outperforms the best approaches and made WoodStock the GGP champion 2016
Liao, Jhan-Yi, and 廖展逸. "Use Upper Confidence Bound for Tree in Chinese Chess." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/b8wbzp.
Повний текст джерела國立東華大學
資訊工程學系
96
Chinese Chess is the one of the oldest game of Chinese. In artificial intelligence, many people study in computer Chinese Chess, There are many theories to get the score of the most bottom board more quickly base on Alpha-beta Search., e.g. internal iterative deepening, history heuristic and killer heuristic. Our method is different from the traditional way of searching. It is base on winning rate and get balance in experience and test other path when it is searching. This search style is like human thinking.
Chou, Cheng-Wei, та 周政緯. "Design and Implementation of a Computer GO Program based on UCㄒ(Upper Confidence bound for Tree search )". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/04854200470817945378.
Повний текст джерела輔仁大學
資訊工程學系
96
Computer Go, one of the related realm of artificial intellegence, Computer Go has been an arduous challenge following chess and Chinese chess.UCT is a tree search algorithm based on Monte Carlo Method, it is very effective that UCT be used in global tree search of Computer Go Program. JIMMY is a Computer Go program, which main principle of design is emulating method of thinking of human. In this paper, we will introduce how to combine JIMMY and UCT to increase the strength of JIMMY.
Siddartha, Y. R. "Learning Tournament Solutions from Preference-based Multi-Armed Bandits." Thesis, 2017. https://etd.iisc.ac.in/handle/2005/4698.
Повний текст джерелаChatterjee, Aritra. "A Study of Thompson Sampling Approach for the Sleeping Multi-Armed Bandit Problem." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/3631.
Повний текст джерелаChatterjee, Aritra. "A Study of Thompson Sampling Approach for the Sleeping Multi-Armed Bandit Problem." Thesis, 2017. http://etd.iisc.ernet.in/2005/3631.
Повний текст джерела方裕欽. "Research on Applicabilities and Improved Strategies of the Upper Confidence Bounds Applied to Trees Algorithm on Othello." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/85144592400294467104.
Повний текст джерелаЧастини книг з теми "Upper Confidence Bound"
Drugan, Mădălina M. "Scalarized Lower Upper Confidence Bound Algorithm." In Lecture Notes in Computer Science, 229–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19084-6_21.
Повний текст джерелаGarivier, Aurélien, and Eric Moulines. "On Upper-Confidence Bound Policies for Switching Bandit Problems." In Lecture Notes in Computer Science, 174–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24412-4_16.
Повний текст джерелаFrancisco-Valencia, Iván, José Raymundo Marcial-Romero, and Rosa María Valdovinos-Rosas. "Some Variations of Upper Confidence Bound for General Game Playing." In Lecture Notes in Computer Science, 68–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21077-9_7.
Повний текст джерелаContal, Emile, David Buffoni, Alexandre Robicquet, and Nicolas Vayatis. "Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration." In Advanced Information Systems Engineering, 225–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40988-2_15.
Повний текст джерелаCarpentier, Alexandra, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, and Peter Auer. "Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits." In Lecture Notes in Computer Science, 189–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24412-4_17.
Повний текст джерелаRoy, Kaushik, Qi Zhang, Manas Gaur, and Amit Sheth. "Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits." In Machine Learning and Knowledge Discovery in Databases. Research Track, 35–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86486-6_3.
Повний текст джерелаHuang, Kuan-Hao, and Hsuan-Tien Lin. "Linear Upper Confidence Bound Algorithm for Contextual Bandit Problem with Piled Rewards." In Advances in Knowledge Discovery and Data Mining, 143–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31750-2_12.
Повний текст джерелаGonçalves, Richard A., Carolina P. Almeida, and Aurora Pozo. "Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D." In Lecture Notes in Computer Science, 411–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15934-8_28.
Повний текст джерелаLiang, Yuan. "Fatigue-Aware Event-Participant Arrangement in Event-Based Social Networks: An Upper Confidence Bound Method." In Lecture Notes in Networks and Systems, 780–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16078-3_54.
Повний текст джерелаWu, Lin, Ying Li, Chao Deng, Lei Chen, Meiyu Yuan, and Hong Jiang. "Implementation and Performance Evaluation of the Fully Enclosed Region Upper Confidence Bound Applied to Trees Algorithm." In Proceedings of the 4th International Conference on Computer Engineering and Networks, 163–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11104-9_19.
Повний текст джерелаТези доповідей конференцій з теми "Upper Confidence Bound"
Ma, Rui, Xvhong Zhou, Xiajing Wang, Zheng Zhang, Jinman Jiang, and Wei Huo. "pAFL: Adaptive Energy Allocation with Upper Confidence Bound." In ICCNS 2023: 2023 13th International Conference on Communication and Network Security. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3638782.3638792.
Повний текст джерелаSaffidine, Abdallah, Tristan Cazenave, and Jean Mehat. "UCD: Upper Confidence Bound for Rooted Directed Acyclic Graphs." In 2010 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2010. http://dx.doi.org/10.1109/taai.2010.79.
Повний текст джерелаMelian-Gutierrez, Laura, Navikkumar Modi, Christophe Moy, Ivan Perez-Alvarez, Faouzi Bader, and Santiago Zazo. "Upper Confidence Bound learning approach for real HF measurements." In 2015 ICC - 2015 IEEE International Conference on Communications Workshops (ICC). IEEE, 2015. http://dx.doi.org/10.1109/iccw.2015.7247209.
Повний текст джерелаBerk, Julian, Sunil Gupta, Santu Rana, and Svetha Venkatesh. "Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/316.
Повний текст джерелаLiu, Guangwu, Wen Shi, and Kun Zhang. "An Upper Confidence Bound Approach to Estimating Coherent Risk Measures." In 2019 Winter Simulation Conference (WSC). IEEE, 2019. http://dx.doi.org/10.1109/wsc40007.2019.9004921.
Повний текст джерелаWan, Yuchen, L. Jeff Hong, and Weiwei Fan. "Upper-Confidence-Bound Procedure for Robust Selection of The Best." In 2023 Winter Simulation Conference (WSC). IEEE, 2023. http://dx.doi.org/10.1109/wsc60868.2023.10407226.
Повний текст джерелаBonnefoi, Remi, Lilian Besson, Julio Manco-Vasquez, and Christophe Moy. "Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions." In 2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW). IEEE, 2019. http://dx.doi.org/10.1109/wcncw.2019.8902891.
Повний текст джерелаJouini, Wassim, Christophe Moy, and Jacques Palicot. "Upper Confidence Bound Algorithm for Opportunistic Spectrum Access with Sensing Errors." In 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications. IEEE, 2011. http://dx.doi.org/10.4108/icst.crowncom.2011.245851.
Повний текст джерелаKao, Kuo-Yuan, and I.-Hao Chen. "Maximal expectation as upper confidence bound for multi-armed bandit problems." In 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2014. http://dx.doi.org/10.1109/itaic.2014.7065060.
Повний текст джерелаImagaw, Takahisa, and Tomoyuki Kaneko. "Estimating the Maximum Expected Value through Upper Confidence Bound of Likelihood." In 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2017. http://dx.doi.org/10.1109/taai.2017.19.
Повний текст джерелаЗвіти організацій з теми "Upper Confidence Bound"
Wright, T. Rare attributes in finite universe: Hypotheses testing specification and exact randomized upper confidence bounds. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/10157055.
Повний текст джерелаWright, T. Rare attributes in finite universe: Hypotheses testing specification and exact randomized upper confidence bounds. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/6379060.
Повний текст джерела