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ORCIDJohannes Ackermann
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2020 – today
- 2024
[j2]Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. RLJ 5: 2140-2161 (2024)- [i4]Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama:
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. CoRR abs/2404.07465 (2024) - [i3]Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. CoRR abs/2405.14114 (2024) - 2022
- [i2]Johannes Ackermann, Minjun Li:
High-Resolution Image Editing via Multi-Stage Blended Diffusion. CoRR abs/2210.12965 (2022) - 2021
- [c2]Johannes Ackermann, Oliver Richter, Roger Wattenhofer:
Unsupervised Task Clustering for Multi-task Reinforcement Learning. ECML/PKDD (1) 2021: 222-237 - 2020
- [j1]Magret Krüger
, Johannes Ackermann, Daniar Osmonov, Veronika Günther, Dirk Bauerschlag, Johannes Hensler, Jan-Hendrik Egberts, Sebastian Lippross, Georgios Gitas, Thomas Becker, Nicolai Maass, Klaus-Peter Jünemann, Ibrahim Alkatout:
Impact of Acoustic and Interactive Disruptive Factors during Robot-Assisted Surgery - A Virtual Surgical Training Model. Sensors 20(20): 5891 (2020) - [c1]Johannes Ackermann, Maximilian Schädler, Christian Blümm:
Convolutional Neural Network Based Blind Estimation of Generalized Mutual Information for Optical Communication. ECOC 2020: 1-4
2010 – 2019
- 2019
- [i1]Johannes Ackermann, Volker Gabler, Takayuki Osa, Masashi Sugiyama:
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics. CoRR abs/1910.01465 (2019)
Coauthor Index
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