Lukas Kirchdorfer

Orcid: 0000-0003-4713-9328

According to our database1, Lukas Kirchdorfer authored at least 14 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Arrival times in dynamic environments: modeling, evaluation, and benchmarking for business process simulation.
Process Sci., December, 2026

Formal Foundations of Agentic Business Process Management.
CoRR, April, 2026

Investigating Uncertainty Weighting for Multi-Task Learning: Insights and Analytical Alternative.
Int. J. Comput. Vis., January, 2026

2025
On the Simplification of Neural Network Architectures for Predictive Process Monitoring.
CoRR, September, 2025

Rethinking BPS: A Utility-Based Evaluation Framework.
CoRR, May, 2025

Discovering multi-agent systems for resource-centric business process simulation.
Process Sci., 2025

Rethinking Business Process Simulation: A Utility-Based Evaluation Framework.
Proceedings of the Business Process Management Forum - BPM 2025 Forum, Seville, Spain, August 31, 2025

A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation.
Proceedings of the Business Process Management - 23rd International Conference, 2025

A Business Process Simulation Tool Bridging Control‑Flow and Resource‑Centric Paradigms.
Proceedings of the Joint Proceedings of the Best Dissertation Award, 2025

2024
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation.
Proceedings of the 6th International Conference on Process Mining, 2024

Analytical Uncertainty-Based Loss Weighting in Multi-task Learning.
Proceedings of the Pattern Recognition, 2024

Examining Common Paradigms in Multi-task Learning.
Proceedings of the Pattern Recognition, 2024

2023
Challenging Common Assumptions in Multi-task Learning.
CoRR, 2023


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