Philipp Altmann

Philipp Altmann, M.Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München,
Institut für Informatik

Oettingenstraße 67
80538 München

Raum E105

Telefon: +49 89 / 2180-9421

Fax: +49 89 / 2180-9148

Mail: philipp.altmann@ifi.lmu.de

Research Interests

  • Collective Intelligence
  • Reinforcement Learning
  • Quantum Machine Learning
  • Surrogate Modeling
  • Explainability

Publications 

  • Philipp Altmann, Céline Davignon, Maximilian Zorn, Fabian Ritz, Claudia Linnhoff-Popien, Thomas Gabor, „REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning,“ in Proceedings of the 16th International Joint Conference on Computational Intelligence (IJCCI 2024), 2024, to appear. [preprint][Code]
  • Philipp Altmann, Julian Schönberger, Maximilian Zorn, Thomas Gabor, „Finding Strong Lottery Ticket Networks with Genetic Algorithms,“ in Proceedings of the 16th International Joint Conference on Computational Intelligence (IJCCI 2024), 2024, to appear. 
  • Philipp Altmann, Julian Schönberger, Steffen Illium, Maximilian Zorn, Fabian Ritz, Tom Haider, Simon Burton, Thomas Gabor, „Emergence in Multi-Agent Systems: A Safety Perspective“, in International Symposium on Leveraging Applications of Formal Methods (ISoLA), 2024, to appear. [preprint]
  • Gerhard Stenzel, Kyrill Schmid, Michael Kölle, Philipp Altmann, Marian Lingsch-Rosenfeld, Maximilian Zorn, Tim Bücher, Thomas Gabor, Martin Wirsing, and Lenz Belzner, „SEGym: Optimizing Large Language Model Assisted Software Engineering Agents with Reinforcement Learning“, in International Symposium on Leveraging Applications of Formal Methods (ISoLA), 2024, to appear.
  • Philipp Altmann, Jonas Stein, Michael Kölle, Adelina Bärligea, Maximilian Zorn, Thomas Gabor, Thomy Phan, Sebastian Feld, Claudia Linnhoff-Popien, „Challenges for Reinforcement Learning in Quantum Circuit Design“, in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 2024, to appear. [preprint][Code]
  • Maximilian Zorn, Jonas Stein, Philipp Altmann, Michael Kölle, Claudia Linnhoff-Popien, and Thomas Gabor, „Cohesive Quantum Circuit Layer Construction with Reinforcement Learning“, in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 2024, to appear.
  • Michael Kölle, Daniel Seidl, Maximilian Zorn, Philipp Altmann, Jonas Stein, and Thomas Gabor, „Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning“, in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 2024, to appear. [preprint]
  • Leo Sünkel, Philipp Altmann, Michael Kölle, and Thomas Gabor, „On the Quantum Impact in Hybrid Classical-Quantum Transfer Learning“, in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 2024, to appear.
  • Gerhard Stenzel, Maximilian Zorn, Philipp Altmann, Maximilian Balthasar Mansky, Michael Kölle, Thomas Gabor, „Self-Replicating Prompts for Large Language Models: Towards Artificial Culture“, in ALIFE 2024: Proceedings of the 2024 Artificial Life Conference, 2024. [DOI][PDF][Code]
  • Maximilian Zorn, Philipp Altmann, Gerhard Stenzel, Michael Kölle, Claudia Linnhoff-Popien, Thomas Gabor, „Self-Adaptive Robustness of Applied Neural-Network-Soups“, in ALIFE 2024: Proceedings of the 2024 Artificial Life Conference, 2024. [DOI][PDF]
  • Maximilian Zorn, Sarah Gerner, Philipp Altmann, and Thomas Gabor, „Final Productive Fitness for Surrogates in Evolutionary Algorithms“, in Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO ’24 Companion, 2024, pp. 583–586. [DOI]
  • Thomy Phan, Felix Sommer, Fabian Ritz, Philipp Altmann, Jonas Nüßlein, Michael Kölle, Lenz Belzner, Claudia Linnhoff-Popien, „Emergent Cooperation from Mutual Acknowledgment Exchange in Multi-Agent Reinforcement Learning“, in Autonomous Agents and Multi-Agent Systems, 2024. [DOI][PDF]
  • Michael Kölle, Timo Witter, Tobias Rohe, Gerhard Stenzel, Philipp Altmann, and Thomas Gabor, „A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning„, in 2024 IEEE International Conference on Quantum Software (QSW), 2024, to appear. [preprint]
  • Philipp Altmann, Adelina Bärligea, Jonas Stein, Michael Kölle, Thomas Gabor, Thomy Phan, and Claudia Linnhoff-Popien, „Quantum Circuit Design: A Reinforcement Learning Challenge“, in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024), 2024. [PDF][Code] 
  • Michael Kölle, Jonas Maurer, Philipp Altmann, Leo Sünkel, Jonas Stein, and Claudia Linnhoff-Popien, „Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence: ICAART, 2024. [DOI][preprint]
  • Leo Sünkel, Philipp Altmann, Michael Kölle, and Thomas Gabor, „Quantum Federated Learning for Image Classification“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence: ICAART, 2024. [DOI]
  • Michael Kölle, Mohamad Hgog, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Stein, and Claudia Linnhoff-Popien, „Quantum Advantage Actor-Critic for Reinforcement Learning“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence: ICAART, 2024. [DOI][preprint]
  • Jonas Stein, Michael Poppel, Philip Adamczyk, Ramona Fabry, Zixin Wu, Michael Kölle, Jonas Nüßlein, Daniëlle Schuman, Philipp Altmann, Thomas Ehmer, Vijay Narasimha, and Claudia Linnhoff-Popien, „Benchmarking Quantum Surrogate Models on Scarce and Noisy Data“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence: ICAART, 2024. [DOI][preprint]
  • Michael Kölle, Tom Schubert, Philipp Altmann, Maximilian Zorn, Jonas Stein, and Claudia Linnhoff-Popien, „A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence: ICAART, 2024. [DOI][preprint]
  • Jonas Stein, Navid Roshani, Maximilian Zorn, Philipp Altmann, Michael Kölle, and Claudia Linnhoff-Popien, „Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence: ICAART, 2024. [DOI][preprint]
  • Michael Kölle, Felix Topp, Thomy Phan, Philipp Altmann, Jonas Nüßlein, and Claudia Linnhoff-Popien, „Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization“, in Proceedings of the 16th International Conference on Agents and Artificial Intelligence: ICAART, 2024. [DOI][preprint]
  • Leo Sünkel, Darya Martyniuk, Julia J. Reichwald, Andrei Morariu, Raja Havish Seggoju, Philipp Altmann, Christoph Roch, Adrian Paschke, „Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans“ in IEEE International Conference on Quantum Computing and Engineering (QCE), 2023. [DOI][preprint]
  • Daniëlle Schuman, Leo Sünkel, Philipp Altmann, Jonas Stein, Christoph Roch, Thomas Gabor, Claudia Linnhoff-Popien, „Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines“, in IEEE International Conference on Quantum Computing and Engineering (QCE), 2023. [DOI][preprint]
  • Philipp Altmann, Fabian Ritz, Leonard Feuchtinger, Jonas Nüßlein, Claudia Linnhof-Popien, and Thomy Phan „CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing“, in 32nd International Joint Conference on Artificial Intelligence (IJCAI ’23), 2023, pp. 3414-3422. [DOI][preprint][source]
  • Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein, Michael Kölle, Thomas Gabor, and Claudia Linnhoff-Popien, „Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability“, in 40th International Conference on Machine Learning (ICML ’23), 2023. [PDF][preprint][source]
  • Philipp Altmann, Thomy Phan, Fabian Ritz, Claudia Linnhoff-Popien, and Thomas Gabor „DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training“, in 15th Adaptive and Learning Agents Workshop (ALA), 2023. [PDF][preprint]
  • Philipp Altmann, Leo Sünkel, Jonas Stein, Tobias Müller, Christoph Roch and Claudia Linnhoff-Popien, “SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training“, in Proceedings of the 15th International Conference on Agents and Artificial Intelligence – Volume 3: ICAART, 2023, pp.744-751. [DOI][PDF][preprint]
  • Michael Kölle, Tim Matheis, Philipp Altmann, and Kyrill Schmid, „Learning to Participate Through Trading of Reward Shares“, in Proceedings of the 15th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART, 2023, pp. 355-362. [DOI][PDF][preprint]
  • Fabian Ritz, Thomy Phan, Andreas Sedlmeier, Philipp Altmann, Jan Wieghardt, Reiner Schmid, Horst Sauer, Cornel Klein, Claudia Linnhoff-Popien and Thomas Gabor, „Capturing Dependencies within Machine Learning via a Formal Process Model“, in Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISOLA 2022, Lecture Notes in Computer Science, vol 13703, Springer International Publishing, pp.249–265. [DOI][preprint]
  • Thomy Phan, Felix Sommer, Philipp Altmann, Fabian Ritz, Lenz Belzner, and Claudia Linnhoff-Popien, „Emergent Cooperation from Mutual Acknowledgment Exchange“, in 21st Conference on Autonomous Agents and Multiagent Systems (AAMAS ’22), 2022, pp. 1047–1055. [PDF][source]
  • Tobias Müller, Christoph Roch, Kyrill Schmid and Philipp Altmann, „Towards Multi-agent Reinforcement Learning using Quantum Boltzmann Machines“, in Proceedings of the 14th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART, 2022, pp. 121-130. [DOI][PDF][preprint]
  • Thomy Phan, Fabian Ritz, Lenz Belzner, Philipp Altmann, Thomas Gabor, and Claudia Linnhoff-Popien, „VAST: Value Function Factorization with Variable Agent Sub-Teams“, in 35th Conference on Neural Information Processing Systems (NeurIPS ’21), 2021. [PDF][source]
  • Thomas Gabor and Philipp Altmann. Benchmarking surrogate-assisted genetic recommender systems. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1568-1575, 2019. [DOI][preprint]

Teaching

Theses

  • Martin Obwexer, Thomas Gabor, Philipp Altmann, Claudia Linnhoff-Popien, „Enhancing Object Recognition with Uncertainty-Based Fusion Techniques: A Comparative Analysis of Voting Techniques and Machine Learning Models“, 2024. 
  • Amelie Trautwein, Philipp Altmann, Maximilian Zorn, Claudia Linnhoff-Popien, „Evolutionary Prompt–optimization Operators for Code-Generation using LLMs“, 2024.
  • David Fischer, Jonas Stein, Jago Silberbauer, Philipp Altmann, Claudia Linnhoff-Popien, „A Path Towards Quantum Advantage for the Unit Commitment Problem“, 2024.
  • Jonas Wild, Maximilian Zorn, Philipp Altmann, Claudia Linnhoff-Popien, „Designing Meta-Rewards for Multi-Agent Reinforcement Learning Cooperation“, 2024.
  • Timo Witter, Michael Kölle, Philipp Altmann, Claudia Linnhoff-Popien, „Improving Variational Quantum Circuits for Hybrid Quantum Proximal Policy Optimization Algorithms„, 2024.
  • Llewellyn Hochhauser, Philipp Altmann, Michael Kölle, Claudia Linnhoff-Popien, „Beeinflussung von Verhalten durch Reward-Manipulation im Multi-Agent Reinforcement-Learning“, 2024.
  • Céline Davignon, Philipp Altmann, Maximilian Zorn, Claudia Linnhoff-Popien, „Portraying Reinforcement Learning Policies via Diverse Behavior selected using Evolutionary Algorithms“, 2024.
  • Simon Hackner, Philipp Altmann, Maximilian Zorn, Claudia Linnhoff-Popien, „Diversity-Driven Pre-Training for Efficient Transfer Reinforcement Learning”, 2023.
  • Katharina Winter, Philipp Altmann, Thomy Phan, Claudia Linnhoff-Popien, „Consensus-Based Mutual Acknowledgment Token Exchange”, 2023.
  • Tom Schubert, Michael Kölle, Philipp Altmann, Claudia Linnhoff-Popien, „A Reinforcement Learning Environment for directed Quantum Circuit Synthesis”, 2023.
  • Sarah Gerner, Thomas Gabor, Philipp Altmann, Claudia Linnhoff-Popien, „Final Productive Fitness in Evolutionary Algorithms and its Approximation via Neural Network Surrogates”, 2023.
  • Jonas Maurer, Michael Kölle, Philipp Altmann, Claudia Linnhoff-Popien, „Dimensionality Reduction with Autoencoders for Efficient Classification with Variational Quantum Circuits”, 2023.
  • Alain Feimer, Thomy Phan, Philipp Altmann, Claudia Linnhoff-Popien, „Generalization in Multi-Agent Reinforcement Learning using Minimax Learning“, 2023.
  • Arnold Unterauer, Thomy Phan, Philipp Altmann, Claudia Linnhoff-Popien, „Hidden Attacks in Multi-Agent Reinforcement Learning“, 2023.
  • Leonard Feuchtinger, Philipp Altmann, Fabian Ritz, Claudia Linnhoff-Popien, „Distributional Shift in Reinforcement Learning – Learning from a single gridworld“, 2022.
  • Marco Börner, Thomy Phan, Philipp Altmann, Claudia Linnhoff-Popien „Predicting the optimal approximation level for Quantum Annealing“, 2022.
  • Felix Sommer, Thomy Phan, Philipp Altmann, Claudia Linnhoff-Popien „Learning Trust in Multi-Agent Systems“, 2020.

Community

  • 11th International Conference on Affective Computing and Intelligent Interaction (ACII 2023): Program Committee
  • 37th Conference on Neural Information Processing Systems (NeurIPS 2023): Reviewer
  • 12th International Conference on Learning Representations (ICLR 2024): Reviewer
  • Neural Computing and Applications: Reviewer
  • 41st International Conference on Machine Learning (ICML 2024): Reviewer
  • 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024): Program Committee
  • 1st Reinforcement Learning Conference (RLC 2024): Reviewer
  • 38th Conference on Neural Information Processing Systems (NeurIPS 2024): Reviewer
  • 39th AAAI Conference on Artificial Intelligence (AAAI 2025): Program Committee
  • 13th International Conference on Learning Representations (ICLR 2025): Reviewer
  • SN Computer Science: Reviewer