Christoph Weinhuber

Orcid: 0000-0002-1600-4933

According to our database1, Christoph Weinhuber authored at least 16 papers between 2021 and 2026.

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Bibliography

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

Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions.
CoRR, February, 2026

Multi-Property Synthesis.
CoRR, January, 2026

Good-for-MDP State Reduction for Stochastic LTL Planning.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Good-for-MDP State Reduction for Stochastic LTL Planning.
Dataset, November, 2025

Code Simulation as a Proxy for High-order Tasks in Large Language Models.
CoRR, February, 2025

Emerson-Lei and Manna-Pnueli Games for LTLf+ and PPLTL+ Synthesis.
Proceedings of the 22nd International Conference on Principles of Knowledge Representation and Reasoning, 2025

Solving MDPs with LTLf+ and PPLTL+ Temporal Objectives.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Explaining Control Policies through Predicate Decision Diagrams.
Proceedings of the 28th ACM International Conference on Hybrid Systems: Computation and Control, 2025

Language-Models-as-a-Service: Overview of a New Paradigm and its Challenges.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
Code Simulation Challenges for Large Language Models.
CoRR, 2024

2023
Federated Learning with Swift: An Extension of Flower and Performance Evaluation.
SoftwareX, December, 2023

The ARRT of Language-Models-as-a-Service: Overview of a New Paradigm and its Challenges.
CoRR, 2023

Is Online Teaching Dead After COVID-19? Student Preferences for Programming Courses.
Proceedings of the 35th International Conference on Software Engineering Education and Training, 2023

2021
dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts (TACAS 21 Artifact).
Dataset, January, 2021

dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts.
Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems, 2021


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