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Artykuły w czasopismach na temat "Algorithme de bandit"
Ciucanu, Radu, Pascal Lafourcade, Gael Marcadet i Marta Soare. "SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits". Journal of Artificial Intelligence Research 73 (23.02.2022): 737–65. http://dx.doi.org/10.1613/jair.1.13163.
Pełny tekst źródłaAzizi, Javad, Branislav Kveton, Mohammad Ghavamzadeh i Sumeet Katariya. "Meta-Learning for Simple Regret Minimization". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 6 (26.06.2023): 6709–17. http://dx.doi.org/10.1609/aaai.v37i6.25823.
Pełny tekst źródłaZhou, Huozhi, Lingda Wang, Lav Varshney i Ee-Peng Lim. "A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 04 (3.04.2020): 6933–40. http://dx.doi.org/10.1609/aaai.v34i04.6176.
Pełny tekst źródłaLi, Youxuan. "Improvement of the recommendation system based on the multi-armed bandit algorithm". Applied and Computational Engineering 36, nr 1 (22.01.2024): 237–41. http://dx.doi.org/10.54254/2755-2721/36/20230453.
Pełny tekst źródłaKuroki, Yuko, Liyuan Xu, Atsushi Miyauchi, Junya Honda i Masashi Sugiyama. "Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback". Neural Computation 32, nr 9 (wrzesień 2020): 1733–73. http://dx.doi.org/10.1162/neco_a_01299.
Pełny tekst źródłaNiño-Mora, José. "A Fast-Pivoting Algorithm for Whittle’s Restless Bandit Index". Mathematics 8, nr 12 (15.12.2020): 2226. http://dx.doi.org/10.3390/math8122226.
Pełny tekst źródłaOswal, Urvashi, Aniruddha Bhargava i Robert Nowak. "Linear Bandits with Feature Feedback". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 04 (3.04.2020): 5331–38. http://dx.doi.org/10.1609/aaai.v34i04.5980.
Pełny tekst źródłaAgarwal, Mridul, Vaneet Aggarwal, Abhishek Kumar Umrawal i Chris Quinn. "DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 8 (18.05.2021): 6557–65. http://dx.doi.org/10.1609/aaai.v35i8.16812.
Pełny tekst źródłaQu, Jiaming. "Survey of dynamic pricing based on Multi-Armed Bandit algorithms". Applied and Computational Engineering 37, nr 1 (22.01.2024): 160–65. http://dx.doi.org/10.54254/2755-2721/37/20230497.
Pełny tekst źródłaWan, Zongqi, Zhijie Zhang, Tongyang Li, Jialin Zhang i Xiaoming Sun. "Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 8 (26.06.2023): 10087–94. http://dx.doi.org/10.1609/aaai.v37i8.26202.
Pełny tekst źródłaRozprawy doktorskie na temat "Algorithme de bandit"
Saadane, Sofiane. "Algorithmes stochastiques pour l'apprentissage, l'optimisation et l'approximation du régime stationnaire". Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30203/document.
Pełny tekst źródłaIn this thesis, we are studying severa! stochastic algorithms with different purposes and this is why we will start this manuscript by giving historicals results to define the framework of our work. Then, we will study a bandit algorithm due to the work of Narendra and Shapiro whose objectif was to determine among a choice of severa! sources which one is the most profitable without spending too much times on the wrong orres. Our goal is to understand the weakness of this algorithm in order to propose an optimal procedure for a quantity measuring the performance of a bandit algorithm, the regret. In our results, we will propose an algorithm called NS over-penalized which allows to obtain a minimax regret bound. A second work will be to understand the convergence in law of this process. The particularity of the algorith is that it converges in law toward a non-diffusive process which makes the study more intricate than the standard case. We will use coupling techniques to study this process and propose rates of convergence. The second work of this thesis falls in the scope of optimization of a function using a stochastic algorithm. We will study a stochastic version of the so-called heavy bali method with friction. The particularity of the algorithm is that its dynamics is based on the ali past of the trajectory. The procedure relies on a memory term which dictates the behavior of the procedure by the form it takes. In our framework, two types of memory will investigated : polynomial and exponential. We will start with general convergence results in the non-convex case. In the case of strongly convex functions, we will provide upper-bounds for the rate of convergence. Finally, a convergence in law result is given in the case of exponential memory. The third part is about the McKean-Vlasov equations which were first introduced by Anatoly Vlasov and first studied by Henry McKean in order to mode! the distribution function of plasma. Our objective is to propose a stochastic algorithm to approach the invariant distribution of the McKean Vlasov equation. Methods in the case of diffusion processes (and sorne more general pro cesses) are known but the particularity of McKean Vlasov process is that it is strongly non-linear. Thus, we will have to develop an alternative approach. We will introduce the notion of asymptotic pseudotrajectory in odrer to get an efficient procedure
Faury, Louis. "Variance-sensitive confidence intervals for parametric and offline bandits". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT046.
Pełny tekst źródłaIn this dissertation we present recent contributions to the problem of optimization under bandit feedback through the design of variance-sensitive confidence intervals. We tackle two distincts topics: (1) the regret minimization task in Generalized Linear Bandits (GLBs), a broad class of non-linear parametric bandits and (2) the problem of off-line policy optimization under bandit feedback. For (1) we study the effects of non-linearity in GLBs and challenge the current understanding that a high level of non-linearity is detrimental to the exploration-exploitation trade-off. We introduce improved algorithms as well as a novel analysis that prove that if correctly handled, the regret minimization task in GLBs is not necessarily harder than for their linear counterparts. It can even be easier for some important members of the GLB family such as the Logistic Bandit. Our approach leverages a new confidence set which captures the non-linearity of the reward signal through its variance, along with a local treatment of the non-linearity through a so-called self-concordance analysis. For (2) we leverage results from the distributionally robust optimization framework to construct asymptotic variance-sensitive confidence intervals for the counterfactual evaluation of policies. This allows to ensure conservatism (sought out by risk-averse agents) while searching off-line for promising policies. Our confidence intervals lead to new counterfactual objectives which, contrary to their predecessors, are more suited for practical deployment thanks to their convex and composite natures
Sani, Amir. "Apprentissage automatique pour la prise de décisions". Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10038/document.
Pełny tekst źródłaStrategic decision-making over valuable resources should consider risk-averse objectives. Many practical areas of application consider risk as central to decision-making. However, machine learning does not. As a result, research should provide insights and algorithms that endow machine learning with the ability to consider decision-theoretic risk. In particular, in estimating decision-theoretic risk on short dependent sequences generated from the most general possible class of processes for statistical inference and through decision-theoretic risk objectives in sequential decision-making. This thesis studies these two problems to provide principled algorithmic methods for considering decision-theoretic risk in machine learning. An algorithm with state-of-the-art performance is introduced for accurate estimation of risk statistics on the most general class of stationary--ergodic processes and risk-averse objectives are introduced in sequential decision-making (online learning) in both the stochastic multi-arm bandit setting and the adversarial full-information setting
Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.
Pełny tekst źródłaCan computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices
Maillard, Odalric-Ambrym. "APPRENTISSAGE SÉQUENTIEL : Bandits, Statistique et Renforcement". Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00845410.
Pełny tekst źródłaDorard, L. R. M. "Bandit algorithms for searching large spaces". Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1348319/.
Pełny tekst źródłaJedor, Matthieu. "Bandit algorithms for recommender system optimization". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.
Pełny tekst źródłaIn this PhD thesis, we study the optimization of recommender systems with the objective of providing more refined suggestions of items for a user to benefit.The task is modeled using the multi-armed bandit framework.In a first part, we look upon two problems that commonly occured in recommendation systems: the large number of items to handle and the management of sponsored contents.In a second part, we investigate the empirical performance of bandit algorithms and especially how to tune conventional algorithm to improve results in stationary and non-stationary environments that arise in practice.This leads us to analyze both theoretically and empirically the greedy algorithm that, in some cases, outperforms the state-of-the-art
Besson, Lilian. "Multi-Players Bandit Algorithms for Internet of Things Networks". Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0005.
Pełny tekst źródłaIn this PhD thesis, we study wireless networks and reconfigurable end-devices that can access Cognitive Radio networks, in unlicensed bands and without central control. We focus on Internet of Things networks (IoT), with the objective of extending the devices’ battery life, by equipping them with low-cost but efficient machine learning algorithms, in order to let them automatically improve the efficiency of their wireless communications. We propose different models of IoT networks, and we show empirically on both numerical simulations and real-world validation the possible gain of our methods, that use Reinforcement Learning. The different network access problems are modeled as Multi-Armed Bandits (MAB), but we found that analyzing the realistic models was intractable, because proving the convergence of many IoT devices playing a collaborative game, without communication nor coordination is hard, when they all follow random activation patterns. The rest of this manuscript thus studies two restricted models, first multi-players bandits in stationary problems, then non-stationary single-player bandits. We also detail another contribution, SMPyBandits, our open-source Python library for numerical MAB simulations, that covers all the studied models and more
Deffayet, Romain. "Bandit Algorithms for Adaptive Modulation and Coding in Wireless Networks". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281884.
Pełny tekst źródłaEfterfrågan på mobilnät av hög kvalitet har ökat mycket de senaste åren och kommer att fortsätta öka under en nära framtid. Detta är resultatet av en ökad mängd trafik på grund av nya användningsfall (HD-videor, live streaming, onlinespel, ...) men kommer också från en diversifiering av trafiken, i synnerhet på grund av kortare och mer frekventa sändningar vilka kan vara på grund av IOT-enheter eller andra telemetri-applikationer. Mobilnätet blir allt komplexare och behovet av bättre hantering av nätverkets egenskaper är högre än någonsin. Den kombinerade effekten av dessa två paradigmer skapar en avvägning: medan man vill utforma algoritmer som uppnår mycket hög prestanda vid beslutsfattning, skulle man också vilja att algoritmerna kan göra det i alla konfigurationer som kan uppstå i detta komplexa nätverk. Istället föreslår denna avhandling att begränsa omfattningen av beslutsalgoritmerna genom att introducera online-inlärning. Avhandlingen fokuserar på första MCS-valet i Adaptiv Modulering och Kodning, där man måste välja en initial överföringshastighet som garanterar snabb kommunikation och minsta möjliga transmissionsfel. Vi formulerar problemet som ett Reinforcement Learning problem och föreslår relevanta begränsningar för matematikt enklare ramverk som Multi-Armed Bandits och Contextual Bandits. Åtta banditalgoritmer testas och granskas med hänsyn till praktisk tillämpning. Avhandlingen visar att en Reinforcement Learning agent kan förbättra användningen av länkkapaciteten mellan sändare och mottagare. Först presenterar vi en Multi-Armed Bandit agent på cell-nivå, som lär sig den optimala initiala MCSen i en given cell och sedan en kontextuell utvidgning av dennaa agent med användarspecifika funktioner. Den föreslagna metoden uppnår en åttaprocentig (8%) ökning av medianhastigheten och en sextiofemprocentig (65%) minskning av median ångern vid skurvis trafik det första 0.5s av tranmissionen, jämfört med ett fast referensvärde.
Degenne, Rémy. "Impact of structure on the design and analysis of bandit algorithms". Thesis, Université de Paris (2019-....), 2019. http://www.theses.fr/2019UNIP7179.
Pełny tekst źródłaIn this Thesis, we study sequential learning problems called stochastic multi-armed bandits. First a new bandit algorithm is presented. The analysis of that algorithm uses confidence intervals on the mean of the arms reward distributions, as most bandit proofs do. In a parametric setting, we derive concentration inequalities which quantify the deviation between the mean parameter of a distribution and its empirical estimation in order to obtain confidence intervals. These inequalities are presented as bounds on the Kullback-Leibler divergence. Three extensions of the stochastic multi-armed bandit problem are then studied. First we study the so-called combinatorial semi-bandit problem, in which an algorithm chooses a set of arms and the reward of each of these arms is observed. The minimal attainable regret then depends on the correlation between the arm distributions. We consider then a setting in which the observation mechanism changes. One source of difficulty of the bandit problem is the scarcity of information: only the arm pulled is observed. We show how to use efficiently eventual supplementary free information (which do not influence the regret). Finally a new family of algorithms is introduced to obtain both regret minimization and est arm identification regret guarantees. Each algorithm of the family realizes a trade-off between regret and time needed to identify the best arm. In a second part we study the so-called pure exploration problem, in which an algorithm is not evaluated on its regret but on the probability that it returns a wrong answer to a question on the arm distributions. We determine the complexity of such problems and design with performance close to that complexity
Książki na temat "Algorithme de bandit"
Braun, Kathrin, i Cordula Kropp, red. In digitaler Gesellschaft. Bielefeld, Germany: transcript Verlag, 2021. http://dx.doi.org/10.14361/9783839454534.
Pełny tekst źródłaBlock, Katharina, Anne Deremetz, Anna Henkel i Malte Rehbein, red. 10 Minuten Soziologie: Digitalisierung. Bielefeld, Germany: transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839457108.
Pełny tekst źródłaBandit Algorithms. Cambridge University Press, 2020.
Znajdź pełny tekst źródłaLattimore, Tor. Bandit Algorithms. University of Cambridge ESOL Examinations, 2020.
Znajdź pełny tekst źródłaWhite, John Myles. Bandit Algorithms for Website Optimization. O'Reilly Media, Incorporated, 2012.
Znajdź pełny tekst źródłaDorota Głowacka. Bandit Algorithms in Information Retrieval. Now Publishers, 2019.
Znajdź pełny tekst źródłaWhite, John Myles. Bandit Algorithms for Website Optimization. O'Reilly Media, Incorporated, 2012.
Znajdź pełny tekst źródłaBandit Algorithms for Website Optimization: Developing, Deploying, and Debugging. O'Reilly Media, 2012.
Znajdź pełny tekst źródłaVerständig, Dan, Christina Kast, Janne Stricker i Andreas Nürnberger, red. Algorithmen und Autonomie. Verlag Barbara Budrich, 2022. http://dx.doi.org/10.3224/84742520.
Pełny tekst źródłaBeyer, Elena, Katharina Erler, Christoph Hartmann, Malte Kramme, Michael F. Müller, Tereza Pertot, Elif Tuna i Felix M. Wilke, red. Privatrecht 2050 - Blick in die digitale Zukunft. Nomos Verlagsgesellschaft mbH & Co. KG, 2020. http://dx.doi.org/10.5771/9783748901723.
Pełny tekst źródłaCzęści książek na temat "Algorithme de bandit"
Cesa-Bianchi, Nicolò. "Multi-armed Bandit Problem". W Encyclopedia of Algorithms, 1356–59. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_768.
Pełny tekst źródłaCesa-Bianchi, Nicolò. "Multi-armed Bandit Problem". W Encyclopedia of Algorithms, 1–5. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_768-1.
Pełny tekst źródłaAudibert, Jean-Yves, Rémi Munos i Csaba Szepesvári. "Tuning Bandit Algorithms in Stochastic Environments". W Lecture Notes in Computer Science, 150–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75225-7_15.
Pełny tekst źródłaHendel, Gregor, Matthias Miltenberger i Jakob Witzig. "Adaptive Algorithmic Behavior for Solving Mixed Integer Programs Using Bandit Algorithms". W Operations Research Proceedings, 513–19. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18500-8_64.
Pełny tekst źródłaPoland, Jan. "FPL Analysis for Adaptive Bandits". W Stochastic Algorithms: Foundations and Applications, 58–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11571155_7.
Pełny tekst źródłaVermorel, Joannès, i Mehryar Mohri. "Multi-armed Bandit Algorithms and Empirical Evaluation". W Machine Learning: ECML 2005, 437–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564096_42.
Pełny tekst źródłaCaelen, Olivier, i Gianluca Bontempi. "Improving the Exploration Strategy in Bandit Algorithms". W Lecture Notes in Computer Science, 56–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92695-5_5.
Pełny tekst źródłaTyagi, Hemant, i Bernd Gärtner. "Continuum Armed Bandit Problem of Few Variables in High Dimensions". W Approximation and Online Algorithms, 108–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08001-7_10.
Pełny tekst źródłaShminke, Boris. "gym-saturation: Gymnasium Environments for Saturation Provers (System description)". W Lecture Notes in Computer Science, 187–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43513-3_11.
Pełny tekst źródłaViappiani, Paolo. "Thompson Sampling for Bayesian Bandits with Resets". W Algorithmic Decision Theory, 399–410. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41575-3_31.
Pełny tekst źródłaStreszczenia konferencji na temat "Algorithme de bandit"
Bouneffouf, Djallel, Irina Rish, Guillermo Cecchi i Raphaël Féraud. "Context Attentive Bandits: Contextual Bandit with Restricted Context". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/203.
Pełny tekst źródłaGao, Ruijiang, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee i Matthew Lease. "Human-AI Collaboration with Bandit Feedback". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/237.
Pełny tekst źródłaLiu, Fang, Sinong Wang, Swapna Buccapatnam i Ness Shroff. "UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/338.
Pełny tekst źródłaGupta, Samarth, Shreyas Chaudhari, Subhojyoti Mukherjee, Gauri Joshi i Osman Yagan. "A Unified Approach to Translate Classical Bandit Algorithms to Structured Bandits". W ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413628.
Pełny tekst źródłaXie, Miao, Wotao Yin i Huan Xu. "AutoBandit: A Meta Bandit Online Learning System". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/719.
Pełny tekst źródłaGuo, Xueying, Xiaoxiao Wang i Xin Liu. "AdaLinUCB: Opportunistic Learning for Contextual Bandits". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/336.
Pełny tekst źródłaYang, Peng, Peilin Zhao i Xin Gao. "Bandit Online Learning on Graphs via Adaptive Optimization". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.
Pełny tekst źródłaPeng, Yi, Miao Xie, Jiahao Liu, Xuying Meng, Nan Li, Cheng Yang, Tao Yao i Rong Jin. "A Practical Semi-Parametric Contextual Bandit". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/450.
Pełny tekst źródłaOu, Mingdong, Nan Li, Shenghuo Zhu i Rong Jin. "Multinomial Logit Bandit with Linear Utility Functions". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/361.
Pełny tekst źródłaCarlsson, Emil, Devdatt Dubhashi i Fredrik D. Johansson. "Thompson Sampling for Bandits with Clustered Arms". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/305.
Pełny tekst źródłaRaporty organizacyjne na temat "Algorithme de bandit"
Marty, Frédéric, i Thierry Warin. Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement. CIRANO, grudzień 2023. http://dx.doi.org/10.54932/iwpg7510.
Pełny tekst źródłaJohansen, Richard A., Christina L. Saltus, Molly K. Reif i Kaytee L. Pokrzywinski. A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing. U.S. Army Engineer Research and Development Center, czerwiec 2022. http://dx.doi.org/10.21079/11681/44523.
Pełny tekst źródłaKwong, Man Kam. Sweeping algorithms for five-point stencils and banded matrices. Office of Scientific and Technical Information (OSTI), czerwiec 1992. http://dx.doi.org/10.2172/10160879.
Pełny tekst źródłaKwong, Man Kam. Sweeping algorithms for five-point stencils and banded matrices. Office of Scientific and Technical Information (OSTI), czerwiec 1992. http://dx.doi.org/10.2172/7276272.
Pełny tekst źródłaAlwan, Iktimal, Dennis D. Spencer i Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, grudzień 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.
Pełny tekst źródłaLumsdaine, A., J. White, D. Webber i A. Sangiovanni-Vincentelli. A Band Relaxation Algorithm for Reliable and Parallelizable Circuit Simulation. Fort Belvoir, VA: Defense Technical Information Center, sierpień 1988. http://dx.doi.org/10.21236/ada200783.
Pełny tekst źródłaChen, Z., S. E. Grasby, C. Deblonde i X. Liu. AI-enabled remote sensing data interpretation for geothermal resource evaluation as applied to the Mount Meager geothermal prospective area. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330008.
Pełny tekst źródłaAnderson, Gerald L., i Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, grudzień 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
Pełny tekst źródłaBorges, Carlos F., i Craig S. Peters. An Algorithm for Computing the Stationary Distribution of a Discrete-Time Birth-and-Death Process with Banded Infinitesimal Generator. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 1995. http://dx.doi.org/10.21236/ada295810.
Pełny tekst źródłaTerrill, Eric J. X-band Observations of Waves, Algorithm Development, and Validation High Resolution Wave-Air-Sea Interaction DRI. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2012. http://dx.doi.org/10.21236/ada574656.
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