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Markus Peters
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2010 – 2019
- 2018
- [j5]Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes:
A scalable preference model for autonomous decision-making. Mach. Learn. 107(6): 1039-1068 (2018) - [c6]Markus Peters, Eric Quadrat, Alexander Nolte, Alexander Wolf, Jörg Miehling, Sandro Wartzack, Wolfgang Leidholdt, Sebastian Bauer, Lars Fritzsche, Sascha Wischniewski:
Biomechanical Digital Human Models: Chances and Challenges to Expand Ergonomic Evaluation. IHSED 2018: 885-890 - 2016
- [j4]Wolfgang Ketter, Markus Peters, John Collins, Alok Gupta:
A Multiagent Competitive Gaming Platform to Address Societal Challenges. MIS Q. 40(2): 447-460 (2016) - [j3]Wolfgang Ketter, Markus Peters, John Collins, Alok Gupta:
Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics. MIS Q. 40(4): 1057-1080 (2016) - 2014
- [c5]Markus Peters, Wolfgang Ketter:
A probabilistic model for learning multi-user preferences with applications to electricitytariff choice. MKWI 2014: 1993-2004 - 2013
- [j2]Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins:
A reinforcement learning approach to autonomous decision-making in smart electricity markets. Mach. Learn. 92(1): 5-39 (2013) - [c4]Wolfgang Ketter, Markus Peters, John Collins:
Autonomous Agents in Future Energy Markets: The 2012 Power Trading Agent Competition. AAAI 2013: 1298-1304 - 2012
- [c3]Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins:
Autonomous Data-Driven Decision-Making in Smart Electricity Markets. ECML/PKDD (2) 2012: 132-147
2000 – 2009
- 2007
- [j1]Mohammed J. Zaki, Markus Peters, Ira Assent, Thomas Seidl:
Clicks: An effective algorithm for mining subspace clusters in categorical datasets. Data Knowl. Eng. 60(1): 51-70 (2007) - 2005
- [c2]Mohammed Javeed Zaki, Markus Peters:
CLICKS: Mining Subspace Clusters in Categorical Data via K-partite Maximal Cliques. ICDE 2005: 355-356 - [c1]Mohammed Javeed Zaki, Markus Peters, Ira Assent, Thomas Seidl:
CLICKS: an effective algorithm for mining subspace clusters in categorical datasets. KDD 2005: 736-742
Coauthor Index
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