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Dimakopoulou's research focuses on the development and application of machine learning algorithms. She has made significant contributions to reinforcement learning and contextual bandits, which are critical for decision-making processes under uncertainty.
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Reinforcement
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Dimakopoulou, M., Improving
Reliability & Efficiency Of Performance Monitoring In Linux, Diploma Thesis, National Technical University of Athens,
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Bibaut, A., Kallus, N., Dimakopoulou, M., Chambaz, A., & van der Laan, M. (2021). Risk
Minimization from Adaptively Collected Data. NeurIPS.
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Bibaut, A., Dimakopoulou, M., Kallus, N., Chambaz, A., & van der Laan, M. (2021). Post-Contextual-Bandit
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Su, Y., Dimakopoulou, M., Krishnamurthy, A., & Dudik, M. (2020). Doubly Robust Off-Policy
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After graduating with a Dipl. Ing. from the School of
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Dimakopoulou, M., Coordinated exploration in concurrent reinforcement learning, PhD Thesis, Stanford
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Dimakopoulou, M., Vlassis, N., & Jebara, T. (2019). Marginal
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