Browsing Computer Science by Author "(ORCID 0000-0001-8806-4508 & YÖK ID 41438) Şensoy, Murat"
Now showing items 1-5 of 5
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Campaign participation prediction with deep learning
Ayvaz, Demet; Aydoğan, Reyhan; Akçura, Munir Tolga; Şensoy, Murat (Elsevier, 2021-08)Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to ... -
Explain to me: Towards understanding privacy decisions
Aycı, G.; Şensoy, Murat; Özgür, A.; Yolum, P. (International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2023)Privacy assistants help users manage their privacy online. Their tasks could vary from detecting privacy violations to recommending sharing actions for content that the user intends to share. Recent work on these tasks are ... -
Handling epistemic and aleatory uncertainties in probabilistic circuits
Cerutti, F.; Kaplan, L. M.; Kimmig, A.; Şensoy, Murat (Springer, 2022-04)When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian ... -
Misclassification risk and uncertainty quantification in deep classifiers
Şensoy, Murat; Saleki, Maryam; Julier, S.; Aydoğan, Reyhan; Reid, J. (IEEE, 2021)In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ... -
Uncertainty-aware deep classifiers using generative models
Şensoy, Murat; Kaplan, L.; Cerutti, F.; Saleki, Maryam (Association for the Advancement of Artificial Intelligence, 2020)Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ...
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