Relevant bibliographies by topics / Algorithme de bandit

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Algorithme de bandit'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Algorithme de bandit"

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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.

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The multi-armed bandit is a reinforcement learning model where a learning agent repeatedly chooses an action (pull a bandit arm) and the environment responds with a stochastic outcome (reward) coming from an unknown distribution associated with the chosen arm. Bandits have a wide-range of application such as Web recommendation systems. We address the cumulative reward maximization problem in a secure federated learning setting, where multiple data owners keep their data stored locally and collaborate under the coordination of a central orchestration server. We rely on cryptographic schemes and
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Tong, Ruoyi. "A survey of the application and technical improvement of the multi-armed bandit." Applied and Computational Engineering 77, no. 1 (2024): 25–31. http://dx.doi.org/10.54254/2755-2721/77/20240631.

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In recent years, the multi-armed bandit (MAB) model has been widely used and has shown excellent performance. This article provides an overview of the applications and technical improvements of the multi-armed bandit machine problem. First, an overview of the multi-armed bandit problem is presented, including the explanation of a general modeling approach and several existing common algorithms, such as -greedy, ETC, UCB, and Thompson sampling. Then, the real-life applications of the multi-armed bandit model are explored, covering the fields of recommender systems, healthcare, and finance. Then
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Azizi, Javad, Branislav Kveton, Mohammad Ghavamzadeh, and Sumeet Katariya. "Meta-Learning for Simple Regret Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6709–17. http://dx.doi.org/10.1609/aaai.v37i6.25823.

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We develop a meta-learning framework for simple regret minimization in bandits. In this framework, a learning agent interacts with a sequence of bandit tasks, which are sampled i.i.d. from an unknown prior distribution, and learns its meta-parameters to perform better on future tasks. We propose the first Bayesian and frequentist meta-learning algorithms for this setting. The Bayesian algorithm has access to a prior distribution over the meta-parameters and its meta simple regret over m bandit tasks with horizon n is mere O(m / √n). On the other hand, the meta simple regret of the frequentist
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Zhou, Huozhi, Lingda Wang, Lav Varshney, and 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, no. 04 (2020): 6933–40. http://dx.doi.org/10.1609/aaai.v34i04.6176.

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We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, GLR-CUCB, which incorporates an efficient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). Our analysis shows that the regret of GLR-CUCB is upper bounded by O(√NKT log T), where N is the number of piecewise-stationary seg
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Li, Youxuan. "Improvement of the recommendation system based on the multi-armed bandit algorithm." Applied and Computational Engineering 36, no. 1 (2024): 237–41. http://dx.doi.org/10.54254/2755-2721/36/20230453.

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In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this
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Kuroki, Yuko, Liyuan Xu, Atsushi Miyauchi, Junya Honda, and Masashi Sugiyama. "Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback." Neural Computation 32, no. 9 (2020): 1733–73. http://dx.doi.org/10.1162/neco_a_01299.

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We study the problem of stochastic multiple-arm identification, where an agent sequentially explores a size-[Formula: see text] subset of arms (also known as a super arm) from given [Formula: see text] arms and tries to identify the best super arm. Most work so far has considered the semi-bandit setting, where the agent can observe the reward of each pulled arm or assumed each arm can be queried at each round. However, in real-world applications, it is costly or sometimes impossible to observe a reward of individual arms. In this study, we tackle the full-bandit setting, where only a noisy obs
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Charniauski, Uladzimir, and Yao Zheng. "Autoregressive Bandits in Near-Unstable or Unstable Environment." American Journal of Undergraduate Research 21, no. 2 (2024): 15–25. http://dx.doi.org/10.33697/ajur.2024.116.

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AutoRegressive Bandits (ARBs) is a novel model of a sequential decision-making problem as an autoregressive (AR) process. In this online learning setting, the observed reward follows an autoregressive process, whose action parameters are unknown to the agent and create an AR dynamic that depends on actions the agent chooses. This study empirically demonstrates how assigning the extreme values of systemic stability indexes and other reward-governing parameters severely impairs the ARBs learning in the respective environment. We show that this algorithm suffers numerically larger regrets of high
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Oswal, Urvashi, Aniruddha Bhargava, and Robert Nowak. "Linear Bandits with Feature Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5331–38. http://dx.doi.org/10.1609/aaai.v34i04.5980.

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This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. The focus of this paper is the development of new theory and algorithms for linear bandits with feature feedback which can achieve regret over time horizon T that scales like k√T, without prior knowledge of which features are relevant nor the number k of relevant features. In comparison, the regret of traditional linear bandits is d√T, where d is the t
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Agarwal, Mridul, Vaneet Aggarwal, Abhishek Kumar Umrawal, and Chris Quinn. "DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6557–65. http://dx.doi.org/10.1609/aaai.v35i8.16812.

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We consider the bandit problem of selecting K out of N arms at each time step. The joint reward can be a non-linear function of the rewards of the selected individual arms. The direct use of a multi-armed bandit algorithm requires choosing among all possible combinations, making the action space large. To simplify the problem, existing works on combinatorial bandits typically assume feedback as a linear function of individual rewards. In this paper, we prove the lower bound for top-K subset selection with bandit feedback with possibly correlated rewards. We present a novel algorithm for the co
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Qu, Jiaming. "Survey of dynamic pricing based on Multi-Armed Bandit algorithms." Applied and Computational Engineering 37, no. 1 (2024): 160–65. http://dx.doi.org/10.54254/2755-2721/37/20230497.

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Dynamic pricing seeks to determine the most optimal selling price for a product or service, taking into account factors like limited supply and uncertain demand. This study aims to provide a comprehensive exploration of dynamic pricing using the multi-armed bandit problem framework in various contexts. The investigation highlights the prevalence of Thompson sampling in dynamic pricing scenarios with a Bayesian backdrop, where the seller possesses prior knowledge of demand functions. On the other hand, in non-Bayesian situations, the Upper Confidence Bound (UCB) algorithm family gains traction
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Dissertations / Theses on the topic "Algorithme de bandit"

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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.

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Dans cette thèse, nous étudions des thématiques autour des algorithmes stochastiques et c'est pour cette raison que nous débuterons ce manuscrit par des éléments généraux sur ces algorithmes en donnant des résultats historiques pour poser les bases de nos travaux. Ensuite, nous étudierons un algorithme de bandit issu des travaux de N arendra et Shapiro dont l'objectif est de déterminer parmi un choix de plusieurs sources laquelle profite le plus à l'utilisateur en évitant toutefois de passer trop de temps à tester celles qui sont moins per­formantes. Notre but est dans un premier temps de comp
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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.

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Cette thèse présente des contributions récentes au problème d’optimisation sous feedback bandit, au travers de la construction d’intervalles de confiance sensibles à la variance. Nous traitons deux aspects distincts du problème: (1) la minimisation du regret pour les bandits à modèle linéaire généralisé (GLBs), une large classe de bandits paramétriques non-linéaires et (2) le problème d’optimisation de politique hors ligne sous signal bandit. Concernant (1) nous étudions les effets de la non-linéarité dans les GLBs et remettons en question la compréhension actuelle selon laquelle des hauts niv
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Sani, Amir. "Apprentissage automatique pour la prise de décisions." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10038/document.

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La prise de décision stratégique concernant des ressources de valeur devrait tenir compte du degré d'aversion au risque. D'ailleurs, de nombreux domaines d'application mettent le risque au cœur de la prise de décision. Toutefois, ce n'est pas le cas de l'apprentissage automatique. Ainsi, il semble essentiel de devoir fournir des indicateurs et des algorithmes dotant l'apprentissage automatique de la possibilité de prendre en considération le risque dans la prise de décision. En particulier, nous souhaiterions pouvoir estimer ce dernier sur de courtes séquences dépendantes générées à partir de
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Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.

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Les ordinateurs peuvent-ils enseigner ? Pour répondre à cette question, la recherche dans les Systèmes Tuteurs Intelligents est en pleine expansion parmi la communauté travaillant sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE). C'est un domaine qui rassemble différentes problématiques et réunit des chercheurs venant de domaines variés, tels que la psychologie, la didactique, les neurosciences et, plus particulièrement, le machine learning. Les technologies numériques deviennent de plus en plus présentes dans la vie quotidienne avec le développement des
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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.

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Cette thèse traite des domaines suivant en Apprentissage Automatique: la théorie des Bandits, l'Apprentissage statistique et l'Apprentissage par renforcement. Son fil rouge est l'étude de plusieurs notions d'adaptation, d'un point de vue non asymptotique : à un environnement ou à un adversaire dans la partie I, à la structure d'un signal dans la partie II, à la structure de récompenses ou à un modèle des états du monde dans la partie III. Tout d'abord nous dérivons une analyse non asymptotique d'un algorithme de bandit à plusieurs bras utilisant la divergence de Kullback-Leibler. Celle-ci perm
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Dorard, L. R. M. "Bandit algorithms for searching large spaces." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1348319/.

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Bandit games consist of single-state environments in which an agent must sequentially choose actions to take, for which rewards are given. The objective being to maximise the cumulated reward, the agent naturally seeks to build a model of the relationship between actions and rewards. The agent must both choose uncertain actions in order to improve its model (exploration), and actions that are believed to yield high rewards according to the model (exploitation). The choice of an action to take is called a play of an arm of the bandit, and the total number of plays may or may not be known in adv
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Jedor, Matthieu. "Bandit algorithms for recommender system optimization." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.

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Dans cette thèse de doctorat, nous étudions l'optimisation des systèmes de recommandation dans le but de fournir des suggestions de produits plus raffinées pour un utilisateur.La tâche est modélisée à l'aide du cadre des bandits multi-bras.Dans une première partie, nous abordons deux problèmes qui se posent fréquemment dans les systèmes de recommandation : le grand nombre d'éléments à traiter et la gestion des contenus sponsorisés.Dans une deuxième partie, nous étudions les performances empiriques des algorithmes de bandit et en particulier comment paramétrer les algorithmes traditionnels pour
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Besson, Lilian. "Multi-Players Bandit Algorithms for Internet of Things Networks." Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0005.

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Dans cette thèse de doctorat, nous étudions les réseaux sans fil et les appareils reconfigurables qui peuvent accéder à des réseaux de type radio intelligente, dans des bandes non licenciées et sans supervision centrale. Nous considérons notamment des réseaux actuels ou futurs de l’Internet des Objets (IoT), avec l’objectif d’augmenter la durée de vie de la batterie des appareils, en les équipant d’algorithmes d’apprentissage machine peu coûteux mais efficaces, qui leur permettent d’améliorer automatiquement l’efficacité de leurs communications sans fil. Nous proposons deux modèles de réseaux
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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.

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The demand for quality cellular network coverage has been increasing significantly in the recent years and will continue its progression throughout the near future. This results from an increase of transmitted data, because of new use cases (HD videos, live streaming, online games, ...), but also from a diversification of the traffic, notably because of shorter and more frequent transmissions which can be due to IOT devices or other telemetry applications. The cellular networks are becoming increasingly complex, and the need for better management of the network’s properties is higher than ever
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Degenne, Rémy. "Impact of structure on the design and analysis of bandit algorithms." Thesis, Université de Paris (2019-....), 2019. http://www.theses.fr/2019UNIP7179.

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Cette thèse porte sur des problèmes d'apprentissage statistique séquentiel, dits bandits stochastiques à plusieurs bras. Dans un premier temps un algorithme de bandit est présenté. L'analyse de cet algorithme, comme la majorité des preuves usuelles de bornes de regret pour algorithmes de bandits, utilise des intervalles de confiance pour les moyennes des bras. Dans un cadre paramétrique,on prouve des inégalités de concentration quantifiant la déviation entre le paramètre d'une distribution et son estimation empirique, afin d'obtenir de tels intervalles. Ces inégalités sont exprimées en fonctio
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Books on the topic "Algorithme de bandit"

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Braun, Kathrin, and Cordula Kropp, eds. In digitaler Gesellschaft. transcript Verlag, 2021. http://dx.doi.org/10.14361/9783839454534.

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Wie verändern sich gesellschaftliche Praktiken und die Chancen demokratischer Technikgestaltung, wenn neben Bürger*innen und Öffentlichkeit auch Roboter, Algorithmen, Simulationen oder selbstlernende Systeme einbezogen und als Beteiligte ernstgenommen werden? Die Beiträger*innen des Bandes untersuchen die Neukonfiguration von Verantwortung und Kontrolle, Wissen, Beteiligungsansprüchen und Kooperationsmöglichkeiten im Umgang mit intelligenten Systemen wie smart grids, Servicerobotern, Routenplanern, Finanzmarktalgorithmen und anderen soziodigitalen Arrangements. Aufgezeigt wird, wie die digital
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Karlsson, Hans O. Atomic and molecular density-of-states by direct Lanczos methods. Acta Universitatis Upsaliensis, 1994.

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Block, Katharina, Anne Deremetz, Anna Henkel, and Malte Rehbein, eds. 10 Minuten Soziologie: Digitalisierung. transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839457108.

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Vom Algorithmus bis zum Sensor umfasst die Digitalisierung eine Vielfalt technologischer Innovationen. Ebenso facettenreich sind die Dimensionen, in denen sie die Gesellschaft transformiert und gleichzeitig von ihr geprägt wird. Die Auswirkungen auf die Kommunikation im öffentlichen Raum, auf die Wissenschaft und Landwirtschaft sowie die Wechselwirkungen mit dem Recht, der Wirtschaft und der Ökologie - die Beitragenden des Bandes gehen diesen und anderen Aspekten von Digitalisierung aus verschiedenen theoretischen Blickwinkeln nach. Damit eröffnen sie Perspektiven, die Digitalisierung als sozi
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Bandit Algorithms. Cambridge University Press, 2020.

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Lattimore, Tor. Bandit Algorithms. University of Cambridge ESOL Examinations, 2020.

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White, John Myles. Bandit Algorithms for Website Optimization. O'Reilly Media, Incorporated, 2012.

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Dorota Głowacka. Bandit Algorithms in Information Retrieval. Now Publishers, 2019.

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White, John Myles. Bandit Algorithms for Website Optimization. O'Reilly Media, Incorporated, 2012.

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Bandit Algorithms for Website Optimization: Developing, Deploying, and Debugging. O'Reilly Media, 2012.

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Verständig, Dan, Christina Kast, Janne Stricker, and Andreas Nürnberger, eds. Algorithmen und Autonomie. Verlag Barbara Budrich, 2022. http://dx.doi.org/10.3224/84742520.

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Wir leben in einer Welt der algorithmischen Sortierung und Entscheidungsfindung. Mathematische Modelle kuratieren unsere sozialen Beziehungen, beeinflussen unsere Wahlen und entscheiden sogar darüber, ob wir ins Gefängnis gehen sollten oder nicht. Aber wie viel wissen wir wirklich über Code, algorithmische Strukturen und deren Wirkweisen? Der Band wendet sich den Fragen der Autonomie im digitalen Zeitalter aus einer interdisziplinären Perspektive zu, indem er Beiträge aus Philosophie, Erziehungs- und Kulturwissenschaft mit der Informatik verbindet.
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Book chapters on the topic "Algorithme de bandit"

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Cesa-Bianchi, Nicolò. "Multi-armed Bandit Problem." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_768.

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Cesa-Bianchi, Nicolò. "Multi-armed Bandit Problem." In Encyclopedia of Algorithms. Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_768-1.

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Lu, Yangyi, Ziping Xu, and Ambuj Tewari. "Bandit Algorithms for Precision Medicine." In Handbook of Statistical Methods for Precision Medicine. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003216223-13.

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Shminke, Boris. "gym-saturation: Gymnasium Environments for Saturation Provers (System description)." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43513-3_11.

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AbstractThis work describes a new version of a previously published Python package — : a collection of OpenAI Gym environments for guiding saturation-style provers based on the given clause algorithm with reinforcement learning. We contribute usage examples with two different provers: Vampire and iProver. We also have decoupled the proof state representation from reinforcement learning per se and provided examples of using a known Python code embedding model as a first-order logic representation. In addition, we demonstrate how environment wrappers can transform a prover into a problem similar
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Audibert, Jean-Yves, Rémi Munos, and Csaba Szepesvári. "Tuning Bandit Algorithms in Stochastic Environments." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75225-7_15.

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Hendel, Gregor, Matthias Miltenberger, and Jakob Witzig. "Adaptive Algorithmic Behavior for Solving Mixed Integer Programs Using Bandit Algorithms." In Operations Research Proceedings. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18500-8_64.

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Poland, Jan. "FPL Analysis for Adaptive Bandits." In Stochastic Algorithms: Foundations and Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11571155_7.

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Vermorel, Joannès, and Mehryar Mohri. "Multi-armed Bandit Algorithms and Empirical Evaluation." In Machine Learning: ECML 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564096_42.

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Caelen, Olivier, and Gianluca Bontempi. "Improving the Exploration Strategy in Bandit Algorithms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92695-5_5.

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Nguyen, Le Minh Duc, Fuhua Lin, and Maiga Chang. "Generating Learning Sequences Using Contextual Bandit Algorithms." In Generative Intelligence and Intelligent Tutoring Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63028-6_26.

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Conference papers on the topic "Algorithme de bandit"

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Gohil, Vasudev, Rahul Kande, Chen Chen, Ahmad-Reza Sadeghi, and Jeyavijayan Rajendran. "MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546726.

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Nakao, Masato, Kensei Hamamoto, Masateru Tsunoda, et al. "On Applying Bandit Algorithm to Fault Localization Techniques." In 2024 IEEE 35th International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE, 2024. https://doi.org/10.1109/issrew63542.2024.00060.

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Li, Miao, Siyi Qiu, Jiong Liu, and Wenping Song. "Content Caching Optimization Based on Improved Bandit Learning Algorithm." In 2024 33rd International Conference on Computer Communications and Networks (ICCCN). IEEE, 2024. http://dx.doi.org/10.1109/icccn61486.2024.10637635.

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Gao, Rongjun. "Optimizing Credit Card Fraud Detection with Multi-Armed Bandit Algorithms." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012956000004508.

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Kakarapalli, Parvish, Devendra Kayande, and Rahul Meshram. "Faster Q-Learning Algorithms for Restless Bandits." In 2024 IEEE 8th International Conference on Information and Communication Technology (CICT). IEEE, 2024. https://doi.org/10.1109/cict64037.2024.10899579.

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Bouneffouf, Djallel, Irina Rish, Guillermo Cecchi, and Raphaël Féraud. "Context Attentive Bandits: Contextual Bandit with Restricted Context." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/203.

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We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling.Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context (TSRC) and the Window
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Gupta, Samarth, Shreyas Chaudhari, Subhojyoti Mukherjee, Gauri Joshi, and Osman Yagan. "A Unified Approach to Translate Classical Bandit Algorithms to Structured Bandits." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413628.

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Gao, Ruijiang, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, and Matthew Lease. "Human-AI Collaboration with Bandit Feedback." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/237.

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Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic decision-making solely centers on the algorithm's performance, while recent work that explores human-machine collaboration has framed the decision-making problems as classification tasks. In this paper, we first propose and then develop a solution for a novel human-machine collaboration problem in a bandit feedback setting. Our solution aims to exploit the human-machine complementarity to maximize decision rewards. W
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Yu, Yingqi, Sijia Zhang, Shaoang Li, Lan Zhang, Wei Xie, and Xiang-Yang Li. "Bandits with Concave Aggregated Reward." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/597.

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Multi-armed bandit is a simple but powerful algorithmic framework, and many effective algorithms have been proposed for various online models. In numerous applications, the decision-maker faces diminishing marginal utility. With non-linear aggregations, those algorithms often have poor regret bounds. Motivated by this, we study a bandit problem with diminishing marginal utility, which we termed the bandits with concave aggregated reward(BCAR). To tackle this problem, we propose two algorithms SW-BCAR and SWUCB-BCAR. Through theoretical analysis, we establish the effectiveness of these algorith
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Lehre, Per Kristian, and Shishen Lin. "Concentration Tail-Bound Analysis of Coevolutionary and Bandit Learning Algorithms." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/767.

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Runtime analysis, as a branch of the theory of AI, studies how the number of iterations algorithms take before finding a solution (its runtime) depends on the design of the algorithm and the problem structure. Drift analysis is a state-of-the-art tool for estimating the runtime of randomised algorithms, such as bandit and evolutionary algorithms. Drift refers roughly to the expected progress towards the optimum per iteration. This paper considers the problem of deriving concentration tail-bounds on the runtime of algorithms. It provides a novel drift theorem that gives precise exponential tail
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Reports on the topic "Algorithme de bandit"

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Marty, Frédéric, and Thierry Warin. Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement. CIRANO, 2023. http://dx.doi.org/10.54932/iwpg7510.

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This paper examines algorithmic collusion from legal and economic perspectives, highlighting the growing role of algorithms in digital markets and their potential for anti-competitive behavior. Using bandit algorithms as a model, traditionally applied in uncertain decision-making contexts, we illuminate the dynamics of implicit collusion without overt communication. Legally, the challenge is discerning and classifying these algorithmic signals, especially as unilateral communications. Economically, distinguishing between rational pricing and collusive patterns becomes intricate with algorithm-
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Johansen, Richard A., Christina L. Saltus, Molly K. Reif, and 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, 2022. http://dx.doi.org/10.21079/11681/44523.

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Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25
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Kwong, Man Kam. Sweeping algorithms for five-point stencils and banded matrices. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10160879.

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Kwong, Man Kam. Sweeping algorithms for five-point stencils and banded matrices. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7276272.

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Lumsdaine, A., J. White, D. Webber, and A. Sangiovanni-Vincentelli. A Band Relaxation Algorithm for Reliable and Parallelizable Circuit Simulation. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200783.

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are ob
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Chen, Z., S. E. Grasby, C. Deblonde, and 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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The objective of this study is to search for features and indicators from the identified geothermal resource sweet spot in the south Mount Meager area that are applicable to other volcanic complexes in the Garibaldi Volcanic Belt. A Landsat 8 multi-spectral band dataset, for a total of 57 images ranging from visible through infrared to thermal infrared frequency channels and covering different years and seasons, were selected. Specific features that are indicative of high geothermal heat flux, fractured permeable zones, and groundwater circulation, the three key elements in exploring for geoth
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Johansen, Richard, Molly Reif, Christina Saltus, and Kaytee Pokrzywinski. A broadscale assessment of Sentinel-2 imagery and the Google Earth Engine for the nationwide mapping of chlorophyll a. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48784.

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Harmful algal blooms degrade water quality and can result in adverse health impacts to humans and wildlife. Monitoring these at scale is difficult due to the lack of coincident data. Additionally, traditional field collection methods are labor- and cost-prohibitive, resulting in disparate data collection not capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to alleviate this by leveraging large, public, water quality databases coupled with open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usabili
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Borges, Carlos F., and Craig S. Peters. An Algorithm for Computing the Stationary Distribution of a Discrete-Time Birth-and-Death Process with Banded Infinitesimal Generator. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada295810.

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