Добірка наукової літератури з теми "Bandit learning"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Bandit learning".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Bandit learning"
Ciucanu, Radu, Pascal Lafourcade, Gael Marcadet, and Marta Soare. "SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits." Journal of Artificial Intelligence Research 73 (February 23, 2022): 737–65. http://dx.doi.org/10.1613/jair.1.13163.
Повний текст джерелаSharaf, Amr, and Hal Daumé III. "Meta-Learning Effective Exploration Strategies for Contextual Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9541–48. http://dx.doi.org/10.1609/aaai.v35i11.17149.
Повний текст джерелаYang, Luting, Jianyi Yang, and Shaolei Ren. "Contextual Bandits with Delayed Feedback and Semi-supervised Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15943–44. http://dx.doi.org/10.1609/aaai.v35i18.17968.
Повний текст джерелаKapoor, Sayash, Kumar Kshitij Patel, and Purushottam Kar. "Corruption-tolerant bandit learning." Machine Learning 108, no. 4 (August 29, 2018): 687–715. http://dx.doi.org/10.1007/s10994-018-5758-5.
Повний текст джерелаCheung, Wang Chi, David Simchi-Levi, and Ruihao Zhu. "Hedging the Drift: Learning to Optimize Under Nonstationarity." Management Science 68, no. 3 (March 2022): 1696–713. http://dx.doi.org/10.1287/mnsc.2021.4024.
Повний текст джерелаDu, Yihan, Siwei Wang, and Longbo Huang. "A One-Size-Fits-All Solution to Conservative Bandit Problems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7254–61. http://dx.doi.org/10.1609/aaai.v35i8.16891.
Повний текст джерелаNarita, Yusuke, Shota Yasui, and Kohei Yata. "Efficient Counterfactual Learning from Bandit Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4634–41. http://dx.doi.org/10.1609/aaai.v33i01.33014634.
Повний текст джерелаLupu, Andrei, Audrey Durand, and Doina Precup. "Leveraging Observations in Bandits: Between Risks and Benefits." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6112–19. http://dx.doi.org/10.1609/aaai.v33i01.33016112.
Повний текст джерелаLopez, Romain, Inderjit S. Dhillon, and Michael I. Jordan. "Learning from eXtreme Bandit Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8732–40. http://dx.doi.org/10.1609/aaai.v35i10.17058.
Повний текст джерелаCaro, Felipe, and Onesun Steve Yoo. "INDEXABILITY OF BANDIT PROBLEMS WITH RESPONSE DELAYS." Probability in the Engineering and Informational Sciences 24, no. 3 (April 23, 2010): 349–74. http://dx.doi.org/10.1017/s0269964810000021.
Повний текст джерелаДисертації з теми "Bandit learning"
Liu, Fang. "Efficient Online Learning with Bandit Feedback." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587680990430268.
Повний текст джерелаKlein, Nicolas. "Learning and Experimentation in Strategic Bandit Problems." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-122728.
Повний текст джерелаTalebi, Mazraeh Shahi Mohammad Sadegh. "Online Combinatorial Optimization under Bandit Feedback." Licentiate thesis, KTH, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181321.
Повний текст джерелаQC 20160201
Lomax, S. E. "Cost-sensitive decision tree learning using a multi-armed bandit framework." Thesis, University of Salford, 2013. http://usir.salford.ac.uk/29308/.
Повний текст джерелаJedor, Matthieu. "Bandit algorithms for recommender system optimization." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.
Повний текст джерелаIn 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
Louëdec, Jonathan. "Stratégies de bandit pour les systèmes de recommandation." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30257/document.
Повний текст джерелаCurrent recommender systems need to recommend items that are relevant to users (exploitation), but they must also be able to continuously obtain new information about items and users (exploration). This is the exploration / exploitation dilemma. Such an environment is part of what is called "reinforcement learning". In the statistical literature, bandit strategies are known to provide solutions to this dilemma. The contributions of this multidisciplinary thesis the adaptation of these strategies to deal with some problems of the recommendation systems, such as the recommendation of several items simultaneously, taking into account the aging of the popularity of an items or the recommendation in real time
Nakhe, Paresh [Verfasser], Martin [Gutachter] Hoefer, and Georg [Gutachter] Schnitger. "On bandit learning and pricing in markets / Paresh Nakhe ; Gutachter: Martin Hoefer, Georg Schnitger." Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2018. http://d-nb.info/1167856740/34.
Повний текст джерелаBesson, Lilian. "Multi-Players Bandit Algorithms for Internet of Things Networks." Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0005.
Повний текст джерелаIn 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
Racey, Deborah Elaine. "EFFECTS OF RESPONSE FREQUENCY CONSTRAINTS ON LEARNING IN A NON-STATIONARY MULTI-ARMED BANDIT TASK." OpenSIUC, 2009. https://opensiuc.lib.siu.edu/dissertations/86.
Повний текст джерелаHren, Jean-Francois. "Planification Optimiste pour Systèmes Déterministes." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2012. http://tel.archives-ouvertes.fr/tel-00845898.
Повний текст джерелаКниги з теми "Bandit learning"
Garofalo, Robert Joseph. Chorale and Shaker dance by John P. Zdechlik: A teaching-learning unit. Ft. Lauderdale, FL: Meredith Music Publications, 1999.
Знайти повний текст джерелаGarofalo, Robert Joseph. Suite française by Darius Milhaud: A teaching-learning unit. Ft. Lauderdale, FL: Meredith Music Publications, 1998.
Знайти повний текст джерелаGarofalo, Robert Joseph. On a hymnsong of Philip Bliss by David R. Holsinger: A teaching/learning unit. Galesville, MD: Meredith Music Publications, 2000.
Знайти повний текст джерелаMirror mind. Toronto, Ont: [T. Woollcott], 2009.
Знайти повний текст джерелаJames, Patterson. Retour au collège: Le pire endroit du monde! Vanves: Hachette romans, 2016.
Знайти повний текст джерелаBubeck, Sébastian, and Cesa-Bianchi Nicolò. Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems. Now Publishers, 2012.
Знайти повний текст джерелаZhao, Qing, and R. Srikant. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Morgan & Claypool Publishers, 2019.
Знайти повний текст джерелаZhao, Qing, and R. Srikant. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Morgan & Claypool Publishers, 2019.
Знайти повний текст джерелаZhao, Qing, and R. Srikant. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Morgan & Claypool Publishers, 2019.
Знайти повний текст джерелаZhao, Qing. Multi-Armed Bandits: Theory and Applications to Online Learning in Networks. Springer International Publishing AG, 2019.
Знайти повний текст джерелаЧастини книг з теми "Bandit learning"
Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Associative Bandit Problem." In Encyclopedia of Machine Learning, 49. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_39.
Повний текст джерелаMannor, Shie, Xin Jin, Jiawei Han, Xin Jin, Jiawei Han, Xin Jin, Jiawei Han, and Xinhua Zhang. "k-Armed Bandit." In Encyclopedia of Machine Learning, 561–63. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_424.
Повний текст джерелаFürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Multi-Armed Bandit." In Encyclopedia of Machine Learning, 699. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_565.
Повний текст джерелаFürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Multi-Armed Bandit Problem." In Encyclopedia of Machine Learning, 699. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_566.
Повний текст джерелаMannor, Shie. "k-Armed Bandit." In Encyclopedia of Machine Learning and Data Mining, 687–90. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_424.
Повний текст джерелаMadani, Omid, Daniel J. Lizotte, and Russell Greiner. "The Budgeted Multi-armed Bandit Problem." In Learning Theory, 643–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_46.
Повний текст джерелаMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bandit Problem with Side Information." In Encyclopedia of Machine Learning, 73. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_54.
Повний текст джерелаMunro, Paul, Hannu Toivonen, Geoffrey I. Webb, Wray Buntine, Peter Orbanz, Yee Whye Teh, Pascal Poupart, et al. "Bandit Problem with Side Observations." In Encyclopedia of Machine Learning, 73. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_55.
Повний текст джерелаAgarwal, Mudit, and Naresh Manwani. "ALBIF: Active Learning with BandIt Feedbacks." In Advances in Knowledge Discovery and Data Mining, 353–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05981-0_28.
Повний текст джерелаVermorel, Joannès, and Mehryar Mohri. "Multi-armed Bandit Algorithms and Empirical Evaluation." In Machine Learning: ECML 2005, 437–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564096_42.
Повний текст джерелаТези доповідей конференцій з теми "Bandit learning"
das Dores, Silvia Cristina Nunes, Carlos Soares, and Duncan Ruiz. "Bandit-Based Automated Machine Learning." In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2018. http://dx.doi.org/10.1109/bracis.2018.00029.
Повний текст джерелаXie, Miao, Wotao Yin, and Huan Xu. "AutoBandit: A Meta Bandit Online Learning System." In 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.
Повний текст джерелаDeng, Kun, Chris Bourke, Stephen Scott, Julie Sunderman, and Yaling Zheng. "Bandit-Based Algorithms for Budgeted Learning." In 2007 7th IEEE International Conference on Data Mining (ICDM '07). IEEE, 2007. http://dx.doi.org/10.1109/icdm.2007.91.
Повний текст джерелаZong, Jun, Ting Liu, Zhaowei Zhu, Xiliang Luo, and Hua Qian. "Social Bandit Learning: Strangers Can Help." In 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020. http://dx.doi.org/10.1109/wcsp49889.2020.9299725.
Повний текст джерелаYang, Luting, Jianyi Yang, and Shaolei Ren. "Multi-Feedback Bandit Learning with Probabilistic Contexts." 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.2020/427.
Повний текст джерелаStrehl, Alexander L., Chris Mesterharm, Michael L. Littman, and Haym Hirsh. "Experience-efficient learning in associative bandit problems." In the 23rd international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143956.
Повний текст джерела"LEARNING TO PLAY K-ARMED BANDIT PROBLEMS." In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003733500740081.
Повний текст джерелаZhao, Zibo, Kiyoshi Nakayama, and Ratnesh Sharma. "Decentralized Transactive Energy Auctions with Bandit Learning." In 2019 IEEE PES Transactive Energy Systems Conference (TESC). IEEE, 2019. http://dx.doi.org/10.1109/tesc.2019.8843371.
Повний текст джерелаDu, Wenbin, Huaqing Jin, Chao Yu, and Guosheng Yin. "Deep Reinforcement Learning for Bandit Arm Localization." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020647.
Повний текст джерелаZhang, Xiaoying, Hong Xie, and John C. S. Lui. "Heterogeneous Information Assisted Bandit Learning: Theory and Application." In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00213.
Повний текст джерелаЗвіти організацій з теми "Bandit learning"
Shum, Matthew, Yingyao Hu, and Yutaka Kayaba. Nonparametric learning rules from bandit experiments: the eyes have it! Institute for Fiscal Studies, June 2010. http://dx.doi.org/10.1920/wp.cem.2010.1510.
Повний текст джерелаLiu, Haoyang, Keqin Liu, and Qing Zhao. Learning in A Changing World: Non-Bayesian Restless Multi-Armed Bandit. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada554798.
Повний текст джерелаOlivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42425.
Повний текст джерелаBecker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42003.
Повний текст джерела