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	Using generative adversarial networks to evaluate robustness of reinforcement learning agents against uncertainties Khayatian, F., Nagy, Z., & Bollinger, A. (2021). Using generative adversarial networks to evaluate robustness of reinforcement learning agents against uncertainties. Energy and Buildings, 251, 111334 (13 pp.). https://doi.org/10.1016/j.enbuild.2021.111334 Detailed Record Published Version
	Unsupervised learning for feature projection: extracting patterns from multidimensional building measurements Xiao, C., Khayatian, F., & Dall'O', G. (2020). Unsupervised learning for feature projection: extracting patterns from multidimensional building measurements. Energy and Buildings, 224, 110228 (18 pp.). https://doi.org/10.1016/j.enbuild.2020.110228 Detailed Record Published Version
	Projections of design implications on energy performance of future cities: a case study from Vienna Orehounig, K., Mahdavi, A., Doppelbauer, E. M., Loibl, W., & Tötzer, T. (2014). Projections of design implications on energy performance of future cities: a case study from Vienna. Sustainable Cities and Society, 12, 92-101. https://doi.org/10.1016/j.scs.2014.03.001 Detailed Record Published Version