I am a first-year Ph.D. student at the Empirical Inference department, supervised by Bernhard Schölkopf. Currently, I am researching methods to make reinforcement learning agents discover and explore causal mechanisms in their environments. My goal is to help to make reinforcement learning more robust and sample-efficient.
ei
Neitz, A., Parascandolo, G., Bauer, S., Schölkopf, B.
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
Advances in Neural Information Processing Systems 31, pages: 9838-9848, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)
ei
Schmid, K., Belzner, L., Kiermeier, M., Neitz, A., Phan, T., Gabor, T., Linnhoff, C.
Risk-Sensitivity in Simulation Based Online Planning
KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, pages: 229-240, (Editors: F. Trollmann and A. Y. Turhan), Springer, Cham, September 2018 (conference)
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