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Artykuły w czasopismach na temat "Bandit algorithm"
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 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 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 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 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 źródłaLiu, Zizhuo. "Investigation of progress and application related to Multi-Armed Bandit algorithms". Applied and Computational Engineering 37, nr 1 (22.01.2024): 155–59. http://dx.doi.org/10.54254/2755-2721/37/20230496.
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 źródłaXue, Bo, Ji Cheng, Fei Liu, Yimu Wang i Qingfu Zhang. "Multiobjective Lipschitz Bandits under Lexicographic Ordering". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 15 (24.03.2024): 16238–46. http://dx.doi.org/10.1609/aaai.v38i15.29558.
Pełny tekst źródłaSharaf, Amr, i Hal Daumé III. "Meta-Learning Effective Exploration Strategies for Contextual Bandits". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 11 (18.05.2021): 9541–48. http://dx.doi.org/10.1609/aaai.v35i11.17149.
Pełny tekst źródłaNobari, Sadegh. "DBA: Dynamic Multi-Armed Bandit Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 9869–70. http://dx.doi.org/10.1609/aaai.v33i01.33019869.
Pełny tekst źródłaRozprawy doktorskie na temat "Bandit algorithm"
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
Zhong, Hongliang. "Bandit feedback in Classification and Multi-objective Optimization". Thesis, Ecole centrale de Marseille, 2016. http://www.theses.fr/2016ECDM0004/document.
Pełny tekst źródłaBandit problems constitute a sequential dynamic allocation problem. The pulling agent has to explore its environment (i.e. the arms) to gather information on the one hand, and it has to exploit the collected clues to increase its rewards on the other hand. How to adequately balance the exploration phase and the exploitation phase is the crux of bandit problems and most of the efforts devoted by the research community from this fields has focused on finding the right exploitation/exploration tradeoff. In this dissertation, we focus on investigating two specific bandit problems: the contextual bandit problems and the multi-objective bandit problems. This dissertation provides two contributions. The first contribution is about the classification under partial supervision, which we encode as a contextual bandit problem with side informa- tion. This kind of problem is heavily studied by researchers working on social networks and recommendation systems. We provide a series of algorithms to solve the Bandit feedback problem that pertain to the Passive-Aggressive family of algorithms. We take advantage of its grounded foundations and we are able to show that our algorithms are much simpler to implement than state-of-the-art algorithms for bandit with partial feedback, and they yet achieve better perfor- mances of classification. For multi-objective multi-armed bandit problem (MOMAB), we propose an effective and theoretically motivated method to identify the Pareto front of arms. We in particular show that we can find all elements of the Pareto front with a minimal budget
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
Dorard, 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
Nicol, Olivier. "Data-driven evaluation of contextual bandit algorithms and applications to dynamic recommendation". Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10211/document.
Pełny tekst źródłaThe context of this thesis work is dynamic recommendation. Recommendation is the action, for an intelligent system, to supply a user of an application with personalized content so as to enhance what is refered to as "user experience" e.g. recommending a product on a merchant website or even an article on a blog. Recommendation is considered dynamic when the content to recommend or user tastes evolve rapidly e.g. news recommendation. Many applications that are of interest to us generates a tremendous amount of data through the millions of online users they have. Nevertheless, using this data to evaluate a new recommendation technique or even compare two dynamic recommendation algorithms is far from trivial. This is the problem we consider here. Some approaches have already been proposed. Nonetheless they were not studied very thoroughly both from a theoretical point of view (unquantified bias, loose convergence bounds...) and from an empirical one (experiments on private data only). In this work we start by filling many blanks within the theoretical analysis. Then we comment on the result of an experiment of unprecedented scale in this area: a public challenge we organized. This challenge along with a some complementary experiments revealed a unexpected source of a huge bias: time acceleration. The rest of this work tackles this issue. We show that a bootstrap-based approach allows to significantly reduce this bias and more importantly to control it
Claeys, Emmanuelle. "Clusterisation incrémentale, multicritères de données hétérogènes pour la personnalisation d’expérience utilisateur". Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAD039.
Pełny tekst źródłaIn many activity sectors (health, online sales,...) designing from scratch an optimal solution for a defined problem (finding a protocol to increase the cure rate, designing a web page to promote the purchase of one or more products,...) is often very difficult or even impossible. In order to face this difficulty, designers (doctors, web designers, production engineers,...) often work incrementally by successive improvements of an existing solution. However, defining the most relevant changes remains a difficult problem. Therefore, a solution adopted more and more frequently is to compare constructively different alternatives (also called variations) in order to determine the best one by an A/B Test. The idea is to implement these alternatives and compare the results obtained, i.e. the respective rewards obtained by each variation. To identify the optimal variation in the shortest possible time, many test methods use an automated dynamic allocation strategy. Its allocate the tested subjects quickly and automatically to the most efficient variation, through a learning reinforcement algorithms (as one-armed bandit methods). These methods have shown their interest in practice but also limitations, including in particular a latency time (i.e. a delay between the arrival of a subject to be tested and its allocation) too long, a lack of explicitness of choices and the integration of an evolving context describing the subject's behaviour before being tested. The overall objective of this thesis is to propose a understable generic A/B test method allowing a dynamic real-time allocation which take into account the temporals static subjects’s characteristics
Książki na temat "Bandit algorithm"
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 "Bandit algorithm"
Liu, Weiwen, Shuai Li i Shengyu Zhang. "Contextual Dependent Click Bandit Algorithm for Web Recommendation". W Lecture Notes in Computer Science, 39–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94776-1_4.
Pełny tekst źródłaNeu, Gergely, i Gábor Bartók. "An Efficient Algorithm for Learning with Semi-bandit Feedback". W Lecture Notes in Computer Science, 234–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40935-6_17.
Pełny tekst źródłaGagliolo, Matteo, i Jürgen Schmidhuber. "Algorithm Selection as a Bandit Problem with Unbounded Losses". W Lecture Notes in Computer Science, 82–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13800-3_7.
Pełny tekst źródłaYou, Shuhua, Quan Liu, Qiming Fu, Shan Zhong i Fei Zhu. "A Bayesian Sarsa Learning Algorithm with Bandit-Based Method". W Neural Information Processing, 108–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26532-2_13.
Pełny tekst źródłaEl Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier i Kevin Tierney. "Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach". W Lecture Notes in Computer Science, 216–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53552-0_22.
Pełny tekst źródłaLarcher, Maxime, Robert Meier i Angelika Steger. "A Simple Optimal Algorithm for the 2-Arm Bandit Problem". W Symposium on Simplicity in Algorithms (SOSA), 365–72. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977585.ch33.
Pełny tekst źródłaBouneffouf, Djallel, Amel Bouzeghoub i Alda Lopes Gançarski. "A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System". W Neural Information Processing, 324–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34487-9_40.
Pełny tekst źródłaAchab, Mastane, Stephan Clémençon, Aurélien Garivier, Anne Sabourin i Claire Vernade. "Max K-Armed Bandit: On the ExtremeHunter Algorithm and Beyond". W Machine Learning and Knowledge Discovery in Databases, 389–404. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_24.
Pełny tekst źródłaMoeini, Mahdi, Oliver Wendt i Linus Krumrey. "Portfolio Optimization by Means of a $$\chi $$ -Armed Bandit Algorithm". W Intelligent Information and Database Systems, 620–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49390-8_60.
Pełny tekst źródłaZhang, Xiaofang, Qian Zhou, Tieke He i Bin Liang. "Con-CNAME: A Contextual Multi-armed Bandit Algorithm for Personalized Recommendations". W Artificial Neural Networks and Machine Learning – ICANN 2018, 326–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01421-6_32.
Pełny tekst źródłaStreszczenia konferencji na temat "Bandit algorithm"
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 źródłaZhang, Xiaoying, Hong Xie, Hang Li i John C.S. Lui. "Conversational Contextual Bandit: Algorithm and Application". W WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366423.3380148.
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 źródłaNie, Keyu, Zezhong Zhang, Ted Tao Yuan, Rong Song i Pauline Berry Burke. "Efficient Multivariate Bandit Algorithm with Path Planning". W 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2020. http://dx.doi.org/10.1109/ictai50040.2020.00023.
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 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 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 źródłaHu, Yi-Qi, Yang Yu i Jun-Da Liao. "Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound 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.2019/351.
Pełny tekst źródłaKiribuchi, Daiki, Myungsook Ko i Takeichiro Nishikawa. "Maintenance Interval Adjustment by Applying the Bandit Algorithm". W 2019 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2019. http://dx.doi.org/10.1109/icit.2019.8755002.
Pełny tekst źródłaRaporty organizacyjne na temat "Bandit algorithm"
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 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 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 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 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.
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.
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