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### Browsing by Author "Nika, Andi"

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Item Open Access Contextual combinatorial volatile multi-armed bandits in compact context spaces(Bilkent University, 2021-07) Nika, AndiShow more We consider the contextual combinatorial volatile multi-armed bandit (CCV-MAB) problem in compact context spaces, simultaneously taking into consideration all of its individual features, thus providing a general framework for solving a wide range of practical problems. We solve CCV-MAB using two approaches. First, we use the so called adaptive discretization technique which sequentially partitions the context space X into ’regions of similarity’ and stores similar statistics corresponding to such regions. Under monotonicity of the expected reward and mild continuity assumptions, for both the expected reward and the expected base arm outcomes, we propose Adap-tive Contextual Combinatorial Upper Confidence Bound (ACC-UCB), an online learn-ing algorithm that uses adaptive discretization and incurs O˜(T ( ¯ +1)/( ¯ +2)+) regret for any > 0, where ¯ represents the approximate optimality dimension related to X . This dimension captures both the benignness of the base arm arrivals and the struc-ture of the expected reward. Second, we impose a Gaussian process (GP) structure on the expected base arms outcomes and thus, using the smoothness of the GP posterior, eliminate the need for adaptive discretization. We propose Optimistic Combinatorial Learning and Optimization with Kernel Upper Confidence Bounds (O’CLOK-UCB) which incurs O˜(K√T γ¯T ) regret, where γ¯T is the maximum information gain associ-ated with the set of base arm contexts that appeared in the first T rounds and K here is the maximum cardinality of any feasible super arm over all rounds. For both methods, we provide experimental results which conclude in the superiority of ACC-UCB over the previous state-of-the-art and of O’CLOCK-UCB over ACC-UCB.Show more Item Open Access Multi-user small base station association via contextual combinatorial volatile bandits(IEEE, 2021-03-09) Qureshi, Muhammad Anjum; Nika, Andi; Tekin, CemShow more We propose an efficient mobility management solution to the problem of assigning small base stations (SBSs) to multiple mobile data users in a heterogeneous setting. We formalize the problem using a novel sequential decision-making model named contextual combinatorial volatile multi-armed bandits (MABs), in which each association is considered as an arm, volatility of an arm is imposed by the dynamic arrivals of the users, and context is the additional information linked with the user and the SBS such as user/SBS distance and the transmission frequency. As the next-generation communications are envisioned to take place over highly dynamic links such as the millimeter wave (mmWave) frequency band, we consider the association problem over an unknown channel distribution with a limited feedback in the form of acknowledgments and under the absence of channel state information (CSI). As the links are unknown and dynamically varying, the assignment problem cannot be solved offline. Thus, we propose an online algorithm which is able to solve the user-SBS association problem in a multi-user and time-varying environment, where the number of users dynamically varies over time. Our algorithm strikes the balance between exploration and exploitation and achieves sublinear in time regret with an optimal dependence on the problem structure and the dynamics of user arrivals and departures. In addition, we demonstrate via numerical experiments that our algorithm achieves significant performance gains compared to several benchmark algorithms.Show more Item Open Access Online context-aware task assignment in mobile crowdsourcing via adaptive discretization(IEEE, 2022-09-22) Elahi, Sepehr; Nika, Andi; Tekin, CemShow more Mobile crowdsourcing is rapidly boosting the Internet of Things revolution. Its natural development leads to an adaptation to various real-world scenarios, thus imposing a need for wide generality on data-processing and task-assigning methods. We consider the task assignment problem in mobile crowdsourcing while taking into consideration the following: (i) we assume that additional information is available for both tasks and workers, such as location, device parameters, or task parameters, and make use of such information; (ii) as an important consequence of the worker-location factor, we assume that some workers may not be available for selection at given times; (iii) the workers' characteristics may change over time. To solve the task assignment problem in this setting, we propose Adaptive Optimistic Matching for Mobile Crowdsourcing (AOM-MC), an online learning algorithm that incurs O~(T(D¯+1)/(D¯+2)+ϵ) regret in T rounds, for any ϵ>0 , under mild continuity assumptions. Here, D¯ is a notion of dimensionality which captures the structure of the problem. We also present extensive simulations that illustrate the advantage of adaptive discretization when compared with uniform discretization, and a time- and location-dependent crowdsourcing simulation using a real-world dataset, clearly demonstrating our algorithm's superiority to the current state-of-the-art and baseline algorithms.Show more Item Open Access The pandemic fusion system for endomorphism algebras of p-permutation modules(Bilkent University, 2018-09) Nika, AndiShow more During the 1980's Puig developed a new approach to modular representation theory, introducing new p-local invariants and thereby extending Green's work on G-algebras. We investigate the Puig category, commenting on its local structure and then introduce a new notion, namely pandemic fusion, which extends the Puig's axioms globally on the G-algebra. Finally we give a sketch of the proof on the existence of some p-permutation FG-module realizing the minimal pandemic fusion system.Show more