Now showing items 1-20 of 31

    • Actionable intelligence and online learning for semantic computing 

      Tekin, Cem; van der Schaar, M. (World Scientific Publishing Company, 2017)
      As the world becomes more connected and instrumented, high dimensional, heterogeneous and time-varying data streams are collected and need to be analyzed on the fly to extract the actionable intelligence from the data ...
    • Adaptive contextual learning for unit commitment in microgrids with renewable energy sources 

      Lee, H. -S.; Tekin, Cem; van der, Schaar, M.; Lee, J. -W. (Institute of Electrical and Electronics Engineers, 2018)
      In this paper, we study a unit commitment (UC) problem where the goal is to minimize the operating costs of a microgrid that involves renewable energy sources. Since traditional UC algorithms use a priori information about ...
    • Adaptive ensemble learning with confidence bounds for personalized diagnosis 

      Tekin, Cem; Yoon, J.; Van Der Schaar, M. (AAAI Press, 2016)
      With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in ...
    • Analysis of thompson sampling for combinatorial multi-armed bandit with probabilistically triggered arms 

      Hüyük, Alihan; Tekin, Cem (PLMR, 2020)
      We analyze the regret of combinatorial Thompson sampling (CTS) for the combinatorial multi-armed bandit with probabilistically triggered arms under the semi-bandit feedback setting. We assume that the learner has access ...
    • The biobjective multiarmed bandit: learning approximate lexicographic optimal allocations 

      Tekin, Cem (TÜBİTAK, 2019)
      We consider a biobjective sequential decision-making problem where an allocation (arm) is called ϵ lexicographic optimal if its expected reward in the first objective is at most ϵ smaller than the highest expected reward, ...
    • Combinatorial multi-armed bandit problem with probabilistically triggered arms: a case with bounded regret 

      Sarıtaç, A. Ömer; Tekin, Cem (IEEE, 2017-11)
      In this paper, we study the combinatorial multi-armed bandit problem (CMAB) with probabilistically triggered arms (PTAs). Under the assumption that the arm triggering probabilities (ATPs) are positive for all arms, we prove ...
    • Context-aware hierarchical online learning for performance maximization in mobile crowdsourcing 

      Muller, S. K.; Tekin, Cem; Schaar, M.; Klein, A. (Institute of Electrical and Electronics Engineers, 2018)
      In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance ...
    • Contextual learning for unit commitment with renewable energy sources 

      Lee, H. -S.; Tekin, Cem; Schaar, M.; Lee, J. -W. (IEEE, 2017)
      In this paper, we study a unit commitment (UC) problem minimizing operating costs of the power system with renewable energy sources. We develop a contextual learning algorithm for UC (CLUC) which learns which UC schedule ...
    • eTutor: online learning for personalized education 

      Tekin, Cem; Braun, J.; Schaar, Mihaela van der (IEEE, 2015-04)
      Given recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity ...
    • Exploiting relevance for online decision-making in high-dimensions 

      Turgay, Eralp; Bulucu, Cem; Tekin, Cem (IEEE, 2020)
      Many sequential decision-making tasks require choosing at each decision step the right action out of the vast set of possibilities by extracting actionable intelligence from high-dimensional data streams. Most of the times, ...
    • Fast learning for dynamic resource allocation in AI-Enabled radio networks 

      Qureshi, Muhammad Anjum; Tekin, Cem (IEEE, 2020)
      Artificial Intelligence (AI)-enabled radios are expected to enhance the spectral efficiency of 5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network resources. However, allocating resources ...
    • Feedback adaptive learning for medical and educational application recommendation 

      Tekin, Cem; Elahi, Sepehr; Van Der Schaar, M. (IEEE, 2020)
      Recommending applications (apps) to improve health or educational outcomes requires long-term planning and adaptation based on the user feedback, as it is imperative to recommend the right app at the right time to improve ...
    • Finding it now: networked classifiers in real-time stream mining systems 

      Ducasse, R.; Tekin, Cem; van der Schaar (Springer, Cham, 2019)
      The aim of this chapter is to describe and optimize the specifications of signal processing systems, aimed at extracting in real time valuable information out of large-scale decentralized datasets. A first section will ...
    • Functional contour-following via haptic perception and reinforcement learning 

      Hellman, R. B.; Tekin, Cem; Schaar, M. V.; Santos, V. J. (Institute of Electrical and Electronics Engineers, 2018)
      Many tasks involve the fine manipulation of objects despite limited visual feedback. In such scenarios, tactile and proprioceptive feedback can be leveraged for task completion. We present an approach for real-time haptic ...
    • Gambler's ruin bandit problem 

      Akbarzadeh, Nima; Tekin, Cem (IEEE, 2017)
      In this paper, we propose a new multi-armed bandit problem called the Gambler's Ruin Bandit Problem (GRBP). In the GRBP, the learner proceeds in a sequence of rounds, where each round is a Markov Decision Process (MDP) ...
    • Generalized global bandit and its application in cellular coverage optimization 

      Shen, C.; Zhou, R.; Tekin, Cem; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers, 2018)
      Motivated by the engineering problem of cellular coverage optimization, we propose a novel multiarmed bandit model called generalized global bandit. We develop a series of greedy algorithms that have the capability to ...
    • Global bandits 

      Atan, O.; Tekin, Cem; Schaar, M. V. D. (Institute of Electrical and Electronics Engineers, 2018)
      Multiarmed bandits (MABs) model sequential decision-making problems, in which a learner sequentially chooses arms with unknown reward distributions in order to maximize its cumulative reward. Most of the prior works on MAB ...
    • Learning traffic congestion by contextual bandit problems for optimum localization 

      Şahin, Ümitcan; Yücesoy, V.; Koç, A.; Tekin, Cem (IEEE, 2017)
      Optimum localization problem, which has a wide range of application areas in real life such as emergency services, command and control systems, warehouse localization, shipment planning, aims to find the best location to ...
    • Multi-objective contextual bandits with a dominant objective 

      Tekin, Cem; Turgay, Eralp (IEEE, 2017)
      In this paper, we propose a new contextual bandit problem with two objectives, where one of the objectives dominates the other objective. Unlike single-objective bandit problems in which the learner obtains a random scalar ...
    • Multi-objective contextual multi-armed bandit with a dominant objective 

      Tekin, Cem; Turgay, Eralp (IEEE, 2018)
      We propose a new multi-objective contextual multiarmed bandit (MAB) problem with two objectives, where one of the objectives dominates the other objective. In the proposed problem, the learner obtains a random reward ...