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In this paper the problem of information dissemination in Mobile Ad-hoc Networks (MANET) is studied. The problem is to disseminate a piece of information, initially held by a distinguished source node, to all nodes in a set defined by some…

Data Structures and Algorithms · Computer Science 2011-06-01 Martin Farach-Colton , Antonio Fernandez Anta , Alessia Milani , Miguel A. Mosteiro , Shmuel Zaks

We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous…

Machine Learning · Computer Science 2015-05-19 Alexandra Carpentier , Michal Valko

The pair-matching problem appears in many applications where one wants to discover good matches between pairs of entities or individuals. Formally, the set of individuals is represented by the nodes of a graph where the edges, unobserved at…

Machine Learning · Statistics 2024-03-06 Christophe Giraud , Yann Issartel , Luc Lehéricy , Matthieu Lerasle

In this paper, the problem of distributed opportunistic channel access in wireless relaying is investigated. A relay network with multiple source-destination pairs and multiple relays is considered. All the source nodes contend through a…

Information Theory · Computer Science 2015-03-19 Zhou Zhang , Hai Jiang

Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost. In this paper we present the adversarial SSP model that also accounts for…

Machine Learning · Computer Science 2022-04-06 Aviv Rosenberg , Yishay Mansour

Multi-armed bandit problems are the predominant theoretical model of exploration-exploitation tradeoffs in learning, and they have countless applications ranging from medical trials, to communication networks, to Web search and advertising.…

Data Structures and Algorithms · Computer Science 2017-09-06 Ashwinkumar Badanidiyuru , Robert Kleinberg , Aleksandrs Slivkins

This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate…

Machine Learning · Statistics 2019-06-04 Sumeet Katariya , Ardhendu Tripathy , Robert Nowak

We study the stochastic Multi-Armed Bandit (MAB) problem with random delays in the feedback received by the algorithm. We consider two settings: the reward-dependent delay setting, where realized delays may depend on the stochastic rewards,…

Machine Learning · Computer Science 2021-06-07 Tal Lancewicki , Shahar Segal , Tomer Koren , Yishay Mansour

In this study, we explore a collaborative multi-agent stochastic linear bandit setting involving a network of $N$ agents that communicate locally to minimize their collective regret while keeping their expected cost under a specified…

Machine Learning · Computer Science 2024-10-24 Amirhossein Afsharrad , Parisa Oftadeh , Ahmadreza Moradipari , Sanjay Lall

We study the distributed multi-agent multi-armed bandit problem with heterogeneous rewards over random communication graphs. Uniquely, at each time step $t$ agents communicate over a time-varying random graph $G_t$ generated by applying the…

Machine Learning · Computer Science 2025-10-28 Jingyuan Liu , Hao Qiu , Lin Yang , Mengfan Xu

We consider the problem of identifying the best arm in stochastic Multi-Armed Bandits (MABs) using a fixed sampling budget. Characterizing the minimal instance-specific error probability for this problem constitutes one of the important…

Machine Learning · Computer Science 2024-02-21 Po-An Wang , Ruo-Chun Tzeng , Alexandre Proutiere

In this paper, we study a joint bandwidth allocation and path selection problem via solving a multi-objective minimization problem under the path cardinality constraints, namely MOPC. Our problem formulation captures various types of…

Signal Processing · Electrical Eng. & Systems 2020-08-11 Jinxin Wang , Fan Zhang , Zhonglin Xie , Gong Zhang , Zaiwen Wen

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, we investigate the problem of path selection and rate allocation for multi-hop self-backhaul millimeter wave (mmWave) networks. Enabling…

Networking and Internet Architecture · Computer Science 2019-03-11 Trung Kien Vu , Mehdi Bennis , Merouane Debbah , Matti Latva-aho

We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active arm, and receives a random reward from a finite set of…

Machine Learning · Computer Science 2019-06-20 Tomer Gafni , Kobi Cohen

Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of non-linear overlap cost that penalizes congestion. Routing becomes increasingly more difficult as the number of selected…

Disordered Systems and Neural Networks · Physics 2012-05-15 Chi Ho Yeung , David Saad

Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interference among units and (ii) using these dynamics to inform treatment allocation in order to…

Machine Learning · Statistics 2026-05-13 Aidan Gleich , Eric Laber , Alexander Volfovsky

We consider a stochastic bandit problem with a possibly infinite number of arms. We write $p^*$ for the proportion of optimal arms and $\Delta$ for the minimal mean-gap between optimal and sub-optimal arms. We characterize the optimal…

Machine Learning · Computer Science 2021-11-08 Rianne de Heide , James Cheshire , Pierre Ménard , Alexandra Carpentier

Cooperation between the nodes of wireless multihop networks can increase communication reliability, reduce energy consumption, and decrease latency. The possible improvements are even greater when nodes perform mutual information…

Information Theory · Computer Science 2009-08-27 Stark C. Draper , Lingjia Liu , Andreas F. Molisch , Jonathan S. Yedidia

We consider minimisation of dynamic regret in non-stationary bandits with a slowly varying property. Namely, we assume that arms' rewards are stochastic and independent over time, but that the absolute difference between the expected…

Machine Learning · Computer Science 2021-10-26 Ramakrishnan Krishnamurthy , Aditya Gopalan

We consider the classical problem of sequential resource allocation where a decision maker must repeatedly divide a budget between several resources, each with diminishing returns. This can be recast as a specific stochastic optimization…

Machine Learning · Statistics 2020-01-17 Xavier Fontaine , Shie Mannor , Vianney Perchet
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