Tobias Enders
According to our database1,
Tobias Enders
authored at least 15 papers
between 2017 and 2024.
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Bibliography
2024
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts.
CoRR, 2024
2023
Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems.
CoRR, 2023
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems.
Proceedings of the Learning for Dynamics and Control Conference, 2023
2022
Why Should I Share? Exploring Benefits of Open Data for Private Sector Organizations.
Proceedings of the 26th Pacific Asia Conference on Information Systems, 2022
2021
Untangling the Open Data Value Paradox: How Organizations Benefit from Revealing Data.
Proceedings of the Innovation durch Informationssysteme, 2021
Proceedings of the 25th Pacific Asia Conference on Information Systems, 2021
Proceedings of the 54th Hawaii International Conference on System Sciences, 2021
2020
Igniting the Spark: Overcoming Organizational Change Resistance to Advance Innovation Adoption - The Case of Data-Driven Services.
Proceedings of the Exploring Service Science - 10th International Conference, 2020
Proceedings of the 28th European Conference on Information Systems, 2020
How to Implement an Open Data Strategy? Analyzing Organizational Change Processes to Enable Value Creation by Revealing Data.
Proceedings of the 28th European Conference on Information Systems, 2020
Proceedings of the Smart Service Management - Design Guidelines and Best Practices, 2020
2019
Priorisierung von Digitalisierungsprojekten entlang der gesamten kundenorientierten Prozesskette im Maschinenbau.
HMD Prax. Wirtsch., 2019
Capturing Value from Data: Exploring Factors Influencing Revenue Model Design for Data-Driven Services.
Proceedings of the Human Practice. Digital Ecologies. Our Future. 14. Internationale Tagung Wirtschaftsinformatik (WI 2019), 2019
2018
Proceedings of the Exploring Service Science - 9th International Conference, 2018
2017
GreedyBoost: An Accurate, Efficient and Flexible Ensemble Method for B2B Recommendations.
Proceedings of the 50th Hawaii International Conference on System Sciences, 2017