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Journal articles on the topic 'Algorithme de bandit'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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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2

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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10

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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Wan, Zongqi, Zhijie Zhang, Tongyang Li, Jialin Zhang, and Xiaoming Sun. "Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10087–94. http://dx.doi.org/10.1609/aaai.v37i8.26202.

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Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon T suffer from the regret of at least the square root of T. In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with the order of the polylog T regrets, exponentially improving the dependence in terms of T. To the best of our knowledge, this is the first provable quantum speedup for regrets of bandit problems and in general exploitation in reinforcement lea

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Fei, Bo. "Comparative analysis and applications of classic multi-armed bandit algorithms and their variants." Applied and Computational Engineering 68, no. 1 (2024): 17–30. http://dx.doi.org/10.54254/2755-2721/68/20241389.

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The multi-armed bandit problem, a pivotal aspect of Reinforcement Learning (RL), presents a classic dilemma in sequential decision-making, balancing exploration with exploitation. Renowned bandit algorithms like Explore-Then-Commit, Epsilon-Greedy, SoftMax, Upper Confidence Bound (UCB), and Thompson Sampling have demonstrated efficacy in addressing this issue. Nevertheless, each algorithm exhibits unique strengths and weaknesses, necessitating a detailed comparative evaluation. This paper executes a series of implementations of various established bandit algorithms and their derivatives, aimin

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Niño-Mora, José. "A Fast-Pivoting Algorithm for Whittle’s Restless Bandit Index." Mathematics 8, no. 12 (2020): 2226. http://dx.doi.org/10.3390/math8122226.

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The Whittle index for restless bandits (two-action semi-Markov decision processes) provides an intuitively appealing optimal policy for controlling a single generic project that can be active (engaged) or passive (rested) at each decision epoch, and which can change state while passive. It further provides a practical heuristic priority-index policy for the computationally intractable multi-armed restless bandit problem, which has been widely applied over the last three decades in multifarious settings, yet mostly restricted to project models with a one-dimensional state. This is due in part t

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Xue, Bo, Ji Cheng, Fei Liu, Yimu Wang, and Qingfu Zhang. "Multiobjective Lipschitz Bandits under Lexicographic Ordering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16238–46. http://dx.doi.org/10.1609/aaai.v38i15.29558.

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This paper studies the multiobjective bandit problem under lexicographic ordering, wherein the learner aims to simultaneously maximize ? objectives hierarchically. The only existing algorithm for this problem considers the multi-armed bandit model, and its regret bound is O((KT)^(2/3)) under a metric called priority-based regret. However, this bound is suboptimal, as the lower bound for single objective multi-armed bandits is Omega(KlogT). Moreover, this bound becomes vacuous when the arm number K is infinite. To address these limitations, we investigate the multiobjective Lipschitz bandit mod

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Du, 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 (2021): 7254–61. http://dx.doi.org/10.1609/aaai.v35i8.16891.

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In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner's reward performance must be at least as well as a given baseline at any time. We propose a One-Size-Fits-All solution to CBPs and present its applications to three encompassed problems, i.e. conservative multi-armed bandits (CMAB), conservative linear bandits (CLB) and conservative contextual combinatorial bandits (CCCB). Different from previous works which consider high probability constraints on the expected reward, we focus on a sample-path constraint on the actual

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Liu, Zizhuo. "Investigation of progress and application related to Multi-Armed Bandit algorithms." Applied and Computational Engineering 37, no. 1 (2024): 155–59. http://dx.doi.org/10.54254/2755-2721/37/20230496.

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This paper discusses four Multi-armed Bandit algorithms: Explore-then-Commit (ETC), Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling algorithm. ETC algorithm aims to spend the majority of rounds on the best arm, but it can lead to a suboptimal outcome if the environment changes rapidly. The Epsilon-Greedy algorithm is designed to explore and exploit simultaneously, while it often tries sub-optimal arm even after the algorithm finds the best arm. Thus, the Epsilon-Greedy algorithm performs well when the environment continuously changes. UCB algorithm is one of the most used M

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Sharaf, Amr, and Hal Daumé III. "Meta-Learning Effective Exploration Strategies for Contextual Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9541–48. http://dx.doi.org/10.1609/aaai.v35i11.17149.

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In contextual bandits, an algorithm must choose actions given ob- served contexts, learning from a reward signal that is observed only for the action chosen. This leads to an exploration/exploitation trade-off: the algorithm must balance taking actions it already believes are good with taking new actions to potentially discover better choices. We develop a meta-learning algorithm, Mêlée, that learns an exploration policy based on simulated, synthetic con- textual bandit tasks. Mêlée uses imitation learning against these simulations to train an exploration policy that can be applied to true con

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Dimakopoulou, Maria, Zhengyuan Zhou, Susan Athey, and Guido Imbens. "Balanced Linear Contextual Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3445–53. http://dx.doi.org/10.1609/aaai.v33i01.33013445.

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Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual bandits with linear payoffs that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias. We provide the first regret bound analyses for linear contextual bandits with balancing and show that our al

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Xiang, Biao. "Optimizing Advertising Efficacy: Implementing Cost-Effective Multi-Armed Bandit Algorithms." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 224–29. http://dx.doi.org/10.54097/tey73e61.

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This paper proposes a novel approach of multi-armed bandit (MAB) algorithms for online advertising across multiple platforms with no budget limitation. The study adapts the application of two algorithms, Cost-Subsidized Upper Confidence Bound Bandit (CS-UCB) and Cost-aware Cascading Upper Confidence Bound Bandit (CC-UCB), with the objective to maximize the click-through rate (CTR) while considering cost efficiency. Departing from traditional budget-constrained models such as Bandits with Knapsacks (BwK), this study assumes a competitive advertising environment where market share and ad exposur

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Zhou, Pengjie, Haoyu Wei, and Huiming Zhang. "Selective Reviews of Bandit Problems in AI via a Statistical View." Mathematics 13, no. 4 (2025): 665. https://doi.org/10.3390/math13040665.

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Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. A key subset includes multi-armed bandit (MAB) and stochastic continuum-armed bandit (SCAB) problems, which model sequential decision-making under uncertainty. This review outlines the foundational models and assumptions of bandit problems, explores non-asymptotic theoretical tools like concentration inequalities and minimax regret bounds, and compares frequentist and Bayesian algorithms for managing exploration–exploita

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Tolpin, David, and Solomon Shimony. "MCTS Based on Simple Rerget." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (2021): 193–99. http://dx.doi.org/10.1609/socs.v3i1.18221.

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UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS),is based on UCB, a policy for the Multi-armed Bandit problem (MAB) thatminimizes the cumulative regret. However, search differs from MAB inthat in MCTS it is usually only the final ``arm pull''that collects a reward, rather than all ``arm pulls''.Therefore, it makes more sense to minimize the simple, rather thancumulative, regret. We introduce policies formulti-armed bandits with lower simpleregret than UCB and develop a two-stage scheme (SR+CR) for MCTSwhich outperforms UCT empirically. We also propose a samplingscheme based

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Buchholz, Simon, Jonas M. Kübler, and Bernhard Schölkopf. "Multi-Armed Bandits and Quantum Channel Oracles." Quantum 9 (March 25, 2025): 1672. https://doi.org/10.22331/q-2025-03-25-1672.

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Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible when the arms and the randomness of the rewards of the arms can be queried in superposition. Here we introduce further bandit models where we only have limited access to the randomness of the rewards, but we can still query the arms in superposition. We show that then the query complexity is the same as for classical algorithms. This generalize

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Zhao, Yunfan, Tonghan Wang, Dheeraj Mysore Nagaraj, Aparna Taneja, and Milind Tambe. "The Bandit Whisperer: Communication Learning for Restless Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23404–13. https://doi.org/10.1609/aaai.v39i22.34508.

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Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynamics. However, classic RMAB models largely overlook the challenges of (systematic) data errors - a common occurrence in real-world scenarios due to factors like varying data collection protocols and intentional noise for differential privacy. We demonstrate that conventional RL algorithms used to train RMABs can struggle to perform well in such settings. To solve this problem, we propose the first communication learni

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Amani, Sanae, and Christos Thrampoulidis. "Decentralized Multi-Agent Linear Bandits with Safety Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6627–35. http://dx.doi.org/10.1609/aaai.v35i8.16820.

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We study decentralized stochastic linear bandits, where a network of N agents acts cooperatively to efficiently solve a linear bandit-optimization problem over a d-dimensional space. For this problem, we propose DLUCB: a fully decentralized algorithm that minimizes the cumulative regret over the entire network. At each round of the algorithm each agent chooses its actions following an upper confidence bound (UCB) strategy and agents share information with their immediate neighbors through a carefully designed consensus procedure that repeats over cycles. Our analysis adjusts the duration of th

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Wang, Zhiyong, Xutong Liu, Shuai Li, and John C. S. Lui. "Efficient Explorative Key-Term Selection Strategies for Conversational Contextual Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10288–95. http://dx.doi.org/10.1609/aaai.v37i8.26225.

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Conversational contextual bandits elicit user preferences by occasionally querying for explicit feedback on key-terms to accelerate learning. However, there are aspects of existing approaches which limit their performance. First, information gained from key-term-level conversations and arm-level recommendations is not appropriately incorporated to speed up learning. Second, it is important to ask explorative key-terms to quickly elicit the user's potential interests in various domains to accelerate the convergence of user preference estimation, which has never been considered in existing works

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Wang, Zhenlin, and Jonathan Scarlett. "Max-Min Grouped Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8603–11. http://dx.doi.org/10.1609/aaai.v36i8.20838.

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In this paper, we introduce a multi-armed bandit problem termed max-min grouped bandits, in which the arms are arranged in possibly-overlapping groups, and the goal is to find a group whose worst arm has the highest mean reward. This problem is of interest in applications such as recommendation systems, and is also closely related to widely-studied robust optimization problems. We present two algorithms based successive elimination and robust optimization, and derive upper bounds on the number of samples to guarantee finding a max-min optimal or near-optimal group, as well as an algorithm-inde

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Zhang, Qianqian. "Real-world Applications of Bandit Algorithms: Insights and Innovations." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 753–58. http://dx.doi.org/10.62051/ge4sk783.

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In the rapidly evolving landscape of decision-making systems, the significance of Multi-Armed Bandit (MAB) algorithms has surged, showcasing a remarkable ability to address the exploration-exploitation dilemma across diverse domains. Originating from the probabilistic and statistical decision-making framework, MAB algorithms have established a critical role by offering a systematic approach to making choices in uncertain environments with limited information. These algorithms ingeniously balance the trade-off between exploiting known resources for immediate gains and exploring new possibilitie

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Yu, Baosheng, Meng Fang, and Dacheng Tao. "Per-Round Knapsack-Constrained Linear Submodular Bandits." Neural Computation 28, no. 12 (2016): 2757–89. http://dx.doi.org/10.1162/neco_a_00887.

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Linear submodular bandits has been proven to be effective in solving the diversification and feature-based exploration problem in information retrieval systems. Considering there is inevitably a budget constraint in many web-based applications, such as news article recommendations and online advertising, we study the problem of diversification under a budget constraint in a bandit setting. We first introduce a budget constraint to each exploration step of linear submodular bandits as a new problem, which we call per-round knapsack-constrained linear submodular bandits. We then define an [Formu

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Yang, 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 (2021): 15943–44. http://dx.doi.org/10.1609/aaai.v35i18.17968.

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Contextual multi-armed bandit (MAB) is a classic online learning problem, where a learner/agent selects actions (i.e., arms) given contextual information and discovers optimal actions based on reward feedback. Applications of contextual bandit have been increasingly expanding, including advertisement, personalization, resource allocation in wireless networks, among others. Nonetheless, the reward feedback is delayed in many applications (e.g., a user may only provide service ratings after a period of time), creating challenges for contextual bandits. In this paper, we address delayed feedback

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Roy Chaudhuri, Arghya, and Shivaram Kalyanakrishnan. "Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10085–92. http://dx.doi.org/10.1609/aaai.v34i06.6566.

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Regret minimisation in stochastic multi-armed bandits is a well-studied problem, for which several optimal algorithms have been proposed. Such algorithms depend on (sufficient statistics of) the empirical reward distributions of the arms to decide which arm to pull next. In this paper, we consider the design of algorithms that are constrained to store statistics from only a bounded number of arms. For bandits with a finite set of arms, we derive a sub-linear upper bound on the regret that decreases with the “arm memory” size M. For instances with a large, possibly infinite, set of arms, we sho

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Kaibel, Chris, and Torsten Biemann. "Rethinking the Gold Standard With Multi-armed Bandits: Machine Learning Allocation Algorithms for Experiments." Organizational Research Methods 24, no. 1 (2019): 78–103. http://dx.doi.org/10.1177/1094428119854153.

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In experiments, researchers commonly allocate subjects randomly and equally to the different treatment conditions before the experiment starts. While this approach is intuitive, it means that new information gathered during the experiment is not utilized until after the experiment has ended. Based on methodological approaches from other scientific disciplines such as computer science and medicine, we suggest machine learning algorithms for subject allocation in experiments. Specifically, we discuss a Bayesian multi-armed bandit algorithm for randomized controlled trials and use Monte Carlo sim

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Xi, Guangyu, Chao Tao, and Yuan Zhou. "Near-Optimal MNL Bandits Under Risk Criteria." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10397–404. http://dx.doi.org/10.1609/aaai.v35i12.17245.

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We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria. Unlike the ordinary expected revenue, risk criteria are more general goals widely used in industries and business. We design algorithms for a broad class of risk criteria, including but not limited to the well-known conditional value-at-risk, Sharpe ratio, and entropy risk, and prove that they suffer a near-optimal regret. As a complement, we also conduct experiments with both synthetic and real data to show the empirical performance of our proposed algorithms.

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Niño-Mora, José. "Restless bandits, partial conservation laws and indexability." Advances in Applied Probability 33, no. 1 (2001): 76–98. http://dx.doi.org/10.1017/s0001867800010648.

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We show that if performance measures in a general stochastic scheduling problem satisfy partial conservation laws (PCL), which extend the generalized conservation laws (GCL) introduced by Bertsimas and Niño-Mora (1996), then the problem is solved optimally by a priority-index policy under a range of admissible linear performance objectives, with both this range and the optimal indices being determined by a one-pass adaptive-greedy algorithm that extends Klimov's: we call such scheduling problems PCL-indexable. We further apply the PCL framework to investigate the indexability property of restl

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Cheung, Wang Chi, David Simchi-Levi, and Ruihao Zhu. "Hedging the Drift: Learning to Optimize Under Nonstationarity." Management Science 68, no. 3 (2022): 1696–713. http://dx.doi.org/10.1287/mnsc.2021.4024.

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We introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic regret bounds for a collection of nonstationary stochastic bandit settings. These settings capture applications such as advertisement allocation, dynamic pricing, and traffic network routing in changing environments. We show how the difficulty posed by the (unknown a priori and possibly adversarial) nonstationarity can be overcome by an unconventional marriage between stochastic and adversarial bandit learning algorithms. Beginning with the linear bandit setting, we design and analyze a sliding window-upp

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Huang, Qikang. "Precision Agriculture Optimization based on Multi-Armed Bandits Algorithm: Wheat Yield Optimization under Different Temperature and Precipitation Conditions." ITM Web of Conferences 73 (2025): 01013. https://doi.org/10.1051/itmconf/20257301013.

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Climate change and the growing unpredictability of environmental elements such as temperature and precipitation present considerable challenges to contemporary agriculture. Data-driven algorithms present promising solutions by offering more precise tools for optimizing crop yields and resource efficiency to tackle these challenges. Among these approaches, the multi-armed bandit (MAB) algorithm effectively balances exploration and exploitation, showcasing considerable potential for optimizing agricultural decision-making. This study investigates four widely utilized Multi-Armed Bandits (MAB) al

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Chen, Panyangjie. "Investigation of selection and application of Multi-Armed Bandit algorithms in recommendation system." Applied and Computational Engineering 34, no. 1 (2024): 185–90. http://dx.doi.org/10.54254/2755-2721/34/20230323.

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The Multi-Armed Bandit (MAB) algorithm holds significant prominence as a recommendation system technique, effectively presenting user-centric content preferences based on the analysis of collected data. However, the application of the basic MAB algorithm in real-world recommendation systems is not without challenges, including issues related to data volume and data processing accuracy. Therefore, the optimization algorithm based on the MAB algorithm is more widely used in the recommendation system. This paper briefly introduces the multi-armed bandit algorithm, that is, the use of MAB in the r

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Varatharajah, Yogatheesan, and Brent Berry. "A Contextual-Bandit-Based Approach for Informed Decision-Making in Clinical Trials." Life 12, no. 8 (2022): 1277. http://dx.doi.org/10.3390/life12081277.

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Clinical trials are conducted to evaluate the efficacy of new treatments. Clinical trials involving multiple treatments utilize the randomization of treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such evaluation is performed in post hoc studies that usually use supervised-learning methods that rely on large amounts of data collected in a randomized fashion. That approach often proves to be suboptimal in that some participants may suffer and even die as a result of having not received the most appropriate treatments during the trial. Reinforcement-

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Chen, Xijin, Kim May Lee, Sofia S. Villar, and David S. Robertson. "Some performance considerations when using multi-armed bandit algorithms in the presence of missing data." PLOS ONE 17, no. 9 (2022): e0274272. http://dx.doi.org/10.1371/journal.pone.0274272.

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When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient all

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Zhao, Boxi. "Performance of Multi-Armed Bandit Algorithms in Dynamic vs. Static Environments: A Comparative Analysis." ITM Web of Conferences 73 (2025): 01016. https://doi.org/10.1051/itmconf/20257301016.

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This paper conducts a comparative analysis of Multi-Armed Bandit (MAB) algorithms, particularly the Upper Confidence Bound (UCB) and Thompson Sampling (TS) algorithms, and focuses on the performance of these algorithms in both static and dynamic environments. Multi-armed bandit algorithms are instrumental in optimizing decision-making problems. While these algorithms have been studied in a static environment where the reward distribution is constant throughout the problem, real-world issues often have an unstable reward distribution, where the reward distribution may change throughout the proc

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Huang, Wen, Lu Zhang, and Xintao Wu. "Achieving Counterfactual Fairness for Causal Bandit." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6952–59. http://dx.doi.org/10.1609/aaai.v36i6.20653.

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In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy. We study how to recommend an item at each step to maximize the expected reward while achieving user-side fairness for customers, i.e., customers who share similar profiles will receive a similar reward regardless of their sensitive attributes and items being recommended. By incorporating causal inference into bandits and adopting soft intervention to model the arm selection st

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Esfandiari, Hossein, Amin Karbasi, Abbas Mehrabian, and Vahab Mirrokni. "Regret Bounds for Batched Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7340–48. http://dx.doi.org/10.1609/aaai.v35i8.16901.

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We present simple algorithms for batched stochastic multi-armed bandit and batched stochastic linear bandit problems. We prove bounds for their expected regrets that improve and extend the best known regret bounds of Gao, Han, Ren, and Zhou (NeurIPS 2019), for any number of batches. In particular, our algorithms in both settings achieve the optimal expected regrets by using only a logarithmic number of batches. We also study the batched adversarial multi-armed bandit problem for the first time and provide the optimal regret, up to logarithmic factors, of any algorithm with predetermined batch

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Sharma, Dravyansh, and Arun Suggala. "Offline-to-Online Hyperparameter Transfer for Stochastic Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20362–70. https://doi.org/10.1609/aaai.v39i19.34243.

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Classic algorithms for stochastic bandits typically use hyperparameters that govern their critical properties such as the trade-off between exploration and exploitation. Tuning these hyperparameters is a problem of great practical significance. However this is a challenging problem and in certain cases is information theoretically impossible. To address this challenge, we consider a practically relevant transfer learning setting where one has access to offline data collected from several bandit problems (tasks) coming from an unknown distribution over the tasks. Our aim is to use this offline

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Wang, Liangxu. "Investigation of frontier Multi-Armed Bandit algorithms and applications." Applied and Computational Engineering 34, no. 1 (2024): 179–84. http://dx.doi.org/10.54254/2755-2721/34/20230322.

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Since the amount of content online is growing exponentially and people's time is limited, there is an urgent need for a high-performance algorithm that can make effective recommendations. This paper will introduce a recommendation system model, a sequential decision model, which is called Multi-Armed Bandit. The main idea of the Multi-Armed Bandit model is that at the beginning of the algorithm, all the recommended items are set to the same weight. In the subsequent recommendation process, the model explores the distribution of each item while changing the weight of each item according to the

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Liu, Ruhao. "Optimizing video click-through rates with bandit algorithms." Applied and Computational Engineering 68, no. 1 (2024): 39–44. http://dx.doi.org/10.54254/2755-2721/68/20241401.

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In recent years, videos have increasingly influenced public perception, making video platforms a focal point of digital consumption. One critical challenge for platform operators is identifying videos that resonate most with users, as user ratings directly reflect viewer preferences and experiences. This study explores the use of bandit algorithms to predict and strategize the overall ratings of various anime videos on the Bilibili platform. Bandit algorithms, a subset of the multi-armed bandit model, dynamically adjust selection strategies based on prior feedback to maximize cumulative reward

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Yan, Xue, Yali Du, Binxin Ru, Jun Wang, Haifeng Zhang, and Xu Chen. "Learning to Identify Top Elo Ratings: A Dueling Bandits Approach." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8797–805. http://dx.doi.org/10.1609/aaai.v36i8.20860.

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The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluating the performance of computerised AI agents. However, an accurate estimation of the Elo rating (for the top players) often requires many rounds of competitions, which can be expensive to carry out. In this paper, to minimize the number of comparisons and to improve the sample efficiency of the Elo evaluation (for top players), we propose an efficient online match scheduling algorithm. Specifically, we identify and m

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Zhu, Siqi. "Navigating Complexity in Collaborative Environments through Innovations in Multi-Agent Multi-Armed Bandit Algorithms." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 747–52. http://dx.doi.org/10.62051/fynwzq88.

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As Multi-Armed Bandit (MAB) applications grow increasingly complex, particularly when multiple agents collaborate or compete, traditional bandit algorithms face fresh challenges, underscoring the rising importance of research in multi-agent multi-armed bandits (MAMAB). Developments in MAMAB algorithms have spurred significant advances across a variety of fields, addressing challenges in dynamic and uncertain environments. This paper offers an exhaustive review of recent progress in MAMAB algorithms, emphasizing major strides in enhancing cooperative decision-making and operational efficiency.

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Ontañón, Santiago. "Combinatorial Multi-armed Bandits for Real-Time Strategy Games." Journal of Artificial Intelligence Research 58 (March 29, 2017): 665–702. http://dx.doi.org/10.1613/jair.5398.

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Games with large branching factors pose a significant challenge for game tree search algorithms. In this paper, we address this problem with a sampling strategy for Monte Carlo Tree Search (MCTS) algorithms called "naive sampling", based on a variant of the Multi-armed Bandit problem called "Combinatorial Multi-armed Bandits" (CMAB). We analyze the theoretical properties of several variants of naive sampling, and empirically compare it against the other existing strategies in the literature for CMABs. We then evaluate these strategies in the context of real-time strategy (RTS) games, a genre o

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Tang, Qiao, Hong Xie, Yunni Xia, Jia Lee, and Qingsheng Zhu. "Robust Contextual Bandits via Bootstrapping." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 12182–89. http://dx.doi.org/10.1609/aaai.v35i13.17446.

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Upper confidence bound (UCB) based contextual bandit algorithms require one to know the tail property of the reward distribution. Unfortunately, such tail property is usually unknown or difficult to specify in real-world applications. Using a tail property heavier than the ground truth leads to a slow learning speed of the contextual bandit algorithm, while using a lighter one may cause the algorithm to diverge. To address this fundamental problem, we develop an estimator (evaluated from historical rewards) for the contextual bandit UCB based on the multiplier bootstrapping technique. We first

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Wu, Jiazhen. "In-depth Exploration and Implementation of Multi-Armed Bandit Models Across Diverse Fields." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 201–5. http://dx.doi.org/10.54097/d3ez0n61.

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This paper presents an in-depth analysis of the Multi-Armed Bandit (MAB) problem, tracing its evolution from its origins in the gambling domain of the 1940s to its current prominence in machine learning and artificial intelligence. The analysis begins with a historical overview, noting key developments like Herbert Robbins' probabilistic framework and the expansion of the problem into strategic decision-making in the 1970s. The emergence of algorithms like the Upper Confidence Bound (UCB) and Thompson Sampling in the late 20th century is highlighted, demonstrating the MAB problem's transition

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Fourati, Fares, Christopher John Quinn, Mohamed-Slim Alouini, and Vaneet Aggarwal. "Combinatorial Stochastic-Greedy Bandit." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12052–60. http://dx.doi.org/10.1609/aaai.v38i11.29093.

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We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the selected set of n arms at each time step t in [T] is observed. SGB adopts an optimized stochastic-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms. Unlike existing methods that explore the entire set of unselected base arms during each selection step, our SGB algorithm samples only an optimized proportion of unselected arms and selects actions from this subset. We

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