QCHALLenge
(Quantum-Classical Hybrid Optimization Algorithms for Logistics
and Production Line Management) is supported by the Federal
Ministry for Economic Affairs and Climate Action
with Partners
LMU, SAP, Siemens, BASF, BMW Group, and AQARIOS, 2023-2026.
Ko-Direktor der Arbeitsgruppe Technologische Wegbereiter und
Data Science der Plattform
Lernende Systeme (PLS) mit Förderung des Bundesministerium
für Bildung und Forschung (BMBF). 2022-
Invited presentation at Imperial College London in the series
"Imperial AI Talks", 2022.
QLindA
(Quantum Reinforcement Learning für industrielle Anwendungen) is
supported by the German Federal Ministry of Education and
Research with Partners Siemens AG, Fraunhofer IIS,
Ostbayerische Technische Hochschule Regensburg, and IQM Germany
GmbH, 2020-2024.
PI in ELISE (European Learning and Intelligent Systems
Excellence), a European Network of AI Excellence Centres, funded
under EU Horizon 2020, 2020-2024.
PyKEEN is our
new PyTorch-based library for knowledge graph embeddings (Project Page, Publication).
PyKEEN evolved out of a collaboration between the LMU, Uni Bonn
and TU Denmark. PyKEEN permits a comparative evaluation
of different embedding approaches, 2021.
PI in the Munich Center for Machine Learning (MCML) 2018-
Research
Interests
Our team has a
long tradition in machine learning for relational structured
domains. Currently our focus is on learning with (temporal)
knowledge graphs, where we also explore quantum computing
solutions. Our interest in cognitive
AI was the reason that we are increasingly exploring multimodal
data, such as texts, images, and videos. Foundation models are
becoming important for our work. Our
ultimate goal is to understand human level intelligence.
Medical Decision Support Systems, Precision Medicine
Reinforcement Learning and Multi-Agent Systems
Quantum Computing
Biography
Volker Tresp is a professor atLudwig Maximilian University of Munich(LMU). He received
his Diploma degree in physics from theUniversity of Göttingenin 1984 and M.Sc.,
M.Phil. and Ph.D. degrees fromYale
Universityin
1986 and 1989, respectively. During his Ph.D., he worked in
Yale’sImage Processing and Analysis Group(IPAG). In 1990, he
joinedSiemenswhere
he has been heading various research teams in machine learning.
In 1997, he became Siemens Inventor of the Year for his
innovations in neural networks research and in 2018 became
the first Siemens Distinguished Research Scientist. He
revolutionized steel processing by pioneering a novel Bayesian
neural network approach that cleverly integrated
real-world data with simulated data from a prior
solution.*** In 1994 he was a visiting scientist at theMassachusetts
Institute of Technologyin
theCenter for Biological and Computational
Learning, working with the
teams of Tomaso Poggio and Michael I. Jordan. He was co-editor
ofAdvances in Neural Information Processing
Systems 13. In 2011, he
was appointed professor in informatics at the LMU, where he
teaches a course on machine learning and where he is leading a
second research team. He is known for his work on Bayesian
machine learning, in particular theBayesian Committee Machineand his work onhierarchical learning with Gaussian
processes. TheIHRM, theSRM, SUNS, andRESCALare
milestones in representation learning for multi-relational
graphs. His team has been doing pioneering work on machine
learning with knowledge graphs, temporal knowledge graphs, and
scene graph analysis. The work on theTensor Brainreflects
his interest in mathematical models for cognition and
neuroscience. In 2020, he became a Fellow of theEuropean
Laboratory for Learning and Intelligent Systems(ELLIS). As
co-director (with Kristian Kersting and Paolo Frasconi), he
leads the ELLIS program "Semantic, Symbolic and Interpretable
Machine Learning".
***Renowned AI researcher, Geoff
Hinton, aptly termed this amalgamation of prior data as
"Priors without Prejudice." The solution also introduced neural network
pretraining: the model was first trained on the existing
prior solution and then fine-tuned using plant-specific
data.
Subsequently, Siemens engineers adeptly tackled
the challenges of concept drift and covariate shift, ensuring
the model's adaptability to changing conditions and
environments. As a result, the project achieved remarkable
success, propelling the business unit to become a global leader
in the process industry. To this day, it stands as one of the
most significant achievements in machine learning for the
process industry. During the Golden Decade of the Conference
on Neural Information Processing Systems (then NIPS, now,
NeurIPS) from 1990-2000, I had 16 papers published in its
proceedings, more than any researcher working in Europe. It
was the most important machine learning conference of that
decade and is leading, even today. We were shallower, but
also deeper. Thanks to my co-authors: R. Hofmann, M.
Roescheisen, J. Hollatz, S. Ahmad, R. Neuneier, M.
Taniguchi, D. Ormoneit, H. G. Zimmermann, T. Briegel, J.
Hollmen
Students
Martino
Pulici, Ludwig Maximilian University
of Munich-B
Ihab Ahmed, Ludwig Maximilian University of Munich-S
Jinhe Bi, Ludwig Maximilian University of
Munich-H
Tong Liu, Ludwig
Maximilian University of Munich
Jingpei
Wu, Ludwig
Maximilian University
of Munich
Yize Sun, Ludwig
Maximilian University of Munich-S
Shuo
Chen, Ludwig
Maximilian University of Munich-S
Bailan He, Ludwig
Maximilian University of Munich-S
Ruotong Liao, Ludwig
Maximilian University of Munich
Haokun Chen, Ludwig Maximilian University of
Munich-S
Yao Zhang, Ludwig Maximilian University of
Munich
Gengyuan Zhang, Ludwig Maximilian University of
Munich
Thomas Decker, Ludwig
Maximilian University of Munich-S
Aneta Koleva, Ludwig Maximilian University of Munich-S
Mutliple, including Diamond Foundry: Martin
Roscheisen was an intern in my team in 1991
Panoratio (Michael
Haft, Reimar Hofmann, 2003--): Deep data exploration
(the founders have left the company)
Horizon Robotics
(Kai Yu, 2015--): The world’s highest-valued
AI-chip unicorn
Xplain
Data (Michael Haft, 2015--): From correlation to causation
to artificial intelligence
Awards and Honors:
Co-author of the Honorable Mention Paper Award at AKBC 2022
(with Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma, Zhen Han)
Co-author of the Best Paper Award, ISWC 2021 (with Mehdi
Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma,
Volker Tresp, and Jens Lehmann)
ELLIS Fellow (2020)
Co-author of the Best Paper Award, IEEE ICHI 2020 (with
Zhiliang Wu, Yinchong Yang, Yunpu Ma, Yushan Liu, Rui Zhao,
Michael Moor)
Co-author of the Student Best Paper Award, ISWC 2017 (with
Stephan Baier und Yunpu Ma)
Best Research Paper Nominee ISWC 2014 (with Denis Krompaß and
Maximilian Nickel)
Winner of the ISWC 2011 Semantic Web Challenge (with Irene
Celino, Daniele Dell'Aglio, Emanuele Della Valle, Marco
Balduini, Yi Huang, Tony Lee, Seon-Ho Kim)
Winner of the ESWC 2011 AI Mashup Challenge (with
Daniele Dell´Aglio, Irene Celino, Emanuele Della Valle, Ralph
Grothmann, Florian Steinke)
Best Paper Runner-up PKDD 2005 (with Shipeng Yu, Kai Yu,
Hans-Peter Kriegel)
PyKEEN is our
new PyTorch-based library for knowledge graph embeddings (Project Page, Publication).
PyKEEN evolved out of a collaboration between the LMU, Uni Bonn
and TU Denmark. PyKEEN permits a comparative evaluation
of different embedding approaches.
Haokun Chen, Hang Li, Yao Zhang, Jinhe Bi, Gengyuan Zhang,
Yueqi Zhang, Philip H.S. Torr, Jindong Gu, Denis Krompass, and
Volker Tresp. FedBiP: Heterogeneous One-Shot Federated Learning
with Personalized Latent Diffusion Models, CVPR 2025.
Gengyuan Zhang, Mang Ling Ada Fok, Jialu Ma, Yan Xia, Philip
H.S. Torr, Daniel Cremers, Volker Tresp, and Jindong Gu.
Localizing Events in Videos with Multimodal Queries, CVPR
2025
Alexander Koebler, Thomas Decker, Ingo Thon, Volker Tresp, and
Florian Buettner. Incremental Uncertainty-aware Performance
Monitoring with Active Labeling Intervention. AISTAT,
2025.
Yao Zhang, Zijian Ma, Yunpu Ma, Zhen Han, Yu Wu, and Volker
Tresp. WebPilot: A Versatile and Autonomous Multi-Agent System
for Web Task Execution with Strategic Exploration, AAAI,
2025
Shuo Chen, Zhen Han, Bailan He, Mark Buckley, Philip Torr,
Volker Tresp, and Jindong Gu. Understanding and Improving
In-Context Learning on Vision-language Models. WACV,
2025
Yao Zhang, Haokun Chen, Ahmed Frikha, Yezi Yang, Denis
Krompass, Gengyuan Zhang, Jindong Gu, and Volker Tresp. CL-Cross
VQA: A Continual Learning Benchmark for Cross-Domain Visual
Question. WACV, 2025
Roberto Amoroso, Gengyuan Zhang, Rajat Koner, Lorenzo Baraldi,
Rita Cucchiara, and Volker Tresp. Perceive, Query & Reason:
Enhancing Video QA with Question-Guided Temporal Queries. WACV,
2025
2024
Zifeng Ding, Jingcheng Wu, Jingpei Wu, Yan Xia, Bo Xiong, and
Volker Tresp. Temporal Fact Reasoning over Hyper-Relational
Knowledge Graphs. EMNLP, 2024
Haowei Zhang, Jianzhe Liu, Zhen Han, Shuo Chen, Bailan He,
Volker Tresp, Zhiqiang Xu, Jindong Gu. Visual Question
Decomposition on Multimodal Large Language Models. EMNLP,
2024
Ruotong Liao, Max Erler, Huiyu Wang, Guangyao Zhai, Gengyuan
Zhang, Yunpu Ma, Volker Tresp. VideoINSTA: Zero-shot Long Video
Understanding via Informative Spatial-Temporal Reasoning with
LLMs. EMNLP, 2024
Hang Li, Chengzhi Shen, Philip H.S. Torr, Volker Tresp,
and Jindong Gu. Self-Discovering Interpretable Diffusion Latent
Directions for Responsible Text-to-Image Generation. CVPR,
2024
Thomas Decker, Ananta R. Bhattarai, Jindong Gu, Volker Tresp,
and Florian Buettner. Provably Better Explanations with
Optimized Aggregation of Feature Attributions. ICML,
2024
Ruotong Liao, Xu Jia, Yangzhe, Yunpu Ma, and Volker Tresp.
"GenTKG: Generative Forecasting on Temporal Knowledge Graph with
Large Language Models". NAACL, 2024
Zifeng Ding, Heling Cai, Jingpei Wu, Yunpu Ma, Ruotong Liao,
Bo Xiong, and Volker Tresp. "zrLLM: Zero-Shot Relational
Learning on Temporal Knowledge Graphs with Large Language
Models". NAACL, 2024
Maximilian Bernhard, Roberto Amoroso, Yannic Kindermann,
Lorenzo Baraldi, Rita Cucchiara, Volker Tresp, and Matthias
Schubert. "What's Outside the Intersection? Fine-Grained Error
Analysis for Semantic Segmentation Beyond IoU." IEEE/CVF
Winter Conference on Applications of Computer Vision(WACV),
2024.
Gengyuan Zhang, Yurui Zhang, Kerui Zhang, and Volker Tresp.
"Can Vision-Language Models be a Good Guesser? Exploring VLMs
for Times and Location Reasoning." IEEE/CVF Winter
Conference on Applications of Computer Vision (WACV),
2024.
Ugur Sahin, Hang Li, Qadeer Khan, Daniel Cremers, and Volker
Tresp. "Enhancing Multimodal Compositional Reasoning of Visual
Language Models with Generative Negative Mining." EEE/CVF
Winter Conference on Applications of Computer Vision
(WACV), 2024.
Haokun Chen, Yao Zhang, Denis Krompass, Jindong Gu, and Volker
Tresp. Feddat: An approach for foundation model finetuning in
multi-modal heterogeneous federated learning. AAAI,
2024.
Thomas Decker, Alexander Koebler, Michael Lebacher, Ingo Thon,
Volker Tresp, and Florian Buettner. Explanatory Model Monitoring
to Understand the Effects of Feature Shifts on Performance. KDD,
2024.
Yize Sun, Jiarui Liu, Yunpu Ma, and Volker Tresp.
Differentiable Quantum Architecture Search For Job Shop
Scheduling Problem. ICASSP, 2024.
Shuo Chen, Zhen Han, Bailan He, Zifeng Ding, Wenqian Yu, Philip
Torr, Volker Tresp, and Jindong Gu. "Red Teaming GPT-4V: Are
GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?." In ICLR
2024 Workshop on Secure and Trustworthy Large Language Models,
2024.
Zefeng Wang, Zhen Han,
Chen, Fan Xue, Zifeng Ding, Xun Xiao, Volker Tresp,
Philip Torr, and Jindong Gu. "Stop reasoning! when multimodal
llms with chain-of-thought reasoning meets adversarial
images." COLM, 2024. arXiv:2402.14899
Shuo Chen, Jindong Gu, Zhen Han, Yunpu Ma, Philip Torr, and
Volker Tresp. "Benchmarking robustness of adaptation methods on
pre-trained vision-language models." Advances in Neural
Information Processing Systems 36 (2024).
Rajat Koner, Gagan Jain, Prateek Jain, Volker Tresp, and Sujoy
Paul. LookupViT: Compressing visual information to a limited
number of tokens. arXiv preprint arXiv:2407.12753 (2024).
Gengyuan Zhang, Mang Ling Ada Fok, Yan Xia, Yansong Tang,
Daniel Cremers, Philip Torr, Volker Tresp, Jindong Gu.
Localizing Events in Videos with Multimodal Queries.
arXiv:2406.10079, 2024.
Aneta Koleva, Martin Ringsquandl, Ahmed Hatem, Thomas Runkler,
and Volker Tresp. "Wiki-TabNER: Advancing Table Interpretation
Through Named Entity Recognition." arXiv preprint
arXiv:2403.04577 (2024).
Bailan He, Yushan Liu, Marcel Hildebrandt, Zifeng Ding,
Yaomengxi Han, Volker Tresp. An Automated Evaluation Framework
for Graph Database Query Generation Leveraging Large Language
Models, 2024.
Yao Zhang, Zijian Ma, Yunpu Ma, Zhen Han, Yu Wu, and Volker
Tresp. WebPilot: A Versatile and Autonomous Multi-Agent System
for Web Task Execution with Strategic Exploration. arXiv
preprint arXiv:2408.15978 (2024).
Zifeng Ding, Yifeng Li, Yuan He, Antonio Norelli, Jingcheng
Wu, Volker Tresp, Yunpu Ma, and Michael Bronstein. DyGMamba:
Efficiently Modeling Long-Term Temporal Dependency on
Continuous-Time Dynamic Graphs with State Space Models. arXiv
preprint arXiv:2408.04713 (2024).
Thomas Decker, Michael Lebacher, Volker Tresp. Does Your
Model Think Like an Engineer? Explainable AI for Bearing Fault
Detection with Deep Learning. IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP),
2023.
Ahmed Frikha, Haokun Chen, Denis Krompaß, Thomas Runkler, and
Volker Tresp. Towards data-free domain generalization. In Asian
Conference on Machine Learning, PMLR, 2023.
Soeren Nolting, Zhen Han, and Volker Tresp. Modeling the
evolution of temporal knowledge graphs with uncertainty. arXiv
preprint arXiv:2301.04977, 2023.
Zifeng Ding, Jingpei Wu, Zongyue Li, Yunpu Ma, and Volker
Tresp. Improving Few-Shot Inductive Learning on Temporal
Knowledge Graphs using Confidence-Augmented Reinforcement
Learning. ECML-PKDD, 2023.
Alessandro Giovagnoli, Volker Tresp, Yunpu Ma, Matthias
Schubert.
QNEAT: Natural Evolution of Variational Quantum Circuit
Architecture. In Proceedings of the Companion
Conference on Genetic and Evolutionary Computation, 2023.
Yushan, Liu, Bailan He, Marcel Hildebrandt, Maximilian
Buchner, Daniela Inzko, Roger Wernert, Emanuel Weigel, Dagmar
Beyer, Martin Berbalk, and Volker Tresp. A Knowledge Graph
Perspective on Supply Chain Resilience. D2R2’23: Second
International Workshop on Linked Data-driven Resilience
Research, 2023.
Tanveer Hannan, Rajat Koner, Maximilian Bernhard, Suprosanna
Shit, Bjoern Menze, Volker Tresp, Matthias Schubert, and Thomas
Seidl. GRAtt-VIS: Gated Residual Attention for Auto Rectifying
Video Instance Segmentation. arXiv:2305.17096, 2023.
Aneta Koleva, Martin Ringsquandl, and Volker Tresp.
Adversarial Attacks on Tables with Entity Swap. Workshops at the
49th International Conference on Very Large Data Bases VLDB,
2023.
Zifeng Ding, Zongyue Li, Ruoxia Qi, Jingpei Wu, Bailan He,
Yunpu Ma, Zhao Meng, Shuo Chen, Ruotong Liao, Zhen Han, and
Volker Tresp. ForecastTKGQuestions: A Benchmark for Temporal
Question Answering and Forecasting over Temporal Knowledge
Graphs. ISWC 2023.
Haokun Chen, Ahmed Frikha, Denis Krompass, Jindong Gu, and
Volker Tresp. FRAug: Tackling Federated Learning with Non-IID
Features via Representation Augmentation. ICCV 2023.
Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding,
Yujia Gu, Heinz Koeppl, Hinrich Schütze, and Volker Tresp.
ECOLA: Enhancing Temporal Knowledge Embeddings with
Contextualized Language Representations. ACL 2023.
Yao Zhang, Yunpu Ma, Thomas Seidl, and Volker Tresp. Adaptive
Multi-Resolution Attention with Linear Complexity. IJCNN,
2023.
Zifeng, Ding, Bailan He, Yunpu Ma, Zhen Han, and Volker Tresp.
Learning Meta Representations of One-shot Relations for Temporal
Knowledge Graph Link Prediction. IJCNN, 2023.
Thomas Decker, Michael Lebacher, and Volker Tresp. "Explaining
Deep Neural Networks for Bearing Fault Detection with Vibration
Concepts." In 2023 IEEE 21st International Conference on
Industrial Informatics (INDIN), pp. 1-6. IEEE, 2023.
Jindong, Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He,
Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, and Philip
Torr. "A systematic survey of prompt engineering on
vision-language foundation models." arXiv preprint
arXiv:2307.12980, 2023.
Volker Tresp, Steffen Udluft, Daniel Hein, Werner Hauptmann,
Martin Leib, Christopher Mutschler, Daniel D. Scherer, and
Wolfgang Mauerer. "Workshop Summary: Quantum Machine Learning."
In 2023 IEEE International Conference on Quantum Computing and
Engineering (QCE), vol. 2 IEEE, 2023.
Zifeng Ding, Yunpu Ma, Bailan He, Jingpei Wu, Zhen Han, and
Volker Tresp. "A simple but powerful graph encoder for temporal
knowledge graph completion." In Intelligent Systems Conference,
2023.
Zifeng Ding, Jingpei Wu, Zongyue Li, Yunpu Ma, and Volker
Tresp. "Improving few-shot inductive learning on temporal
knowledge graphs using confidence-augmented reinforcement
learning." In Joint European Conference on Machine Learning and
Knowledge Discovery in Databases (ECML-PKDD), 2023.
Yize Sun, Yunpu Ma, and Volker Tresp. "Differentiable quantum
architecture search for quantum reinforcement learning." In 2023
IEEE International Conference on Quantum Computing and
Engineering (QCE), 2023.
Ruotong Liao, Xu Jia, Yunpu Ma, and Volker Tresp. "GenTKG:
Generative Forecasting on Temporal Knowledge Graph." In Temporal
Graph Learning Workshop@ NeurIPS 2023. 2023.
Yuanchun Shen, Ruotong Liao, Zhen Han, Yunpu Ma, and Volker
Tresp. "GraphextQA: A Benchmark for Evaluating Graph-Enhanced
Large Language Models." arXiv preprint arXiv:2310.08487 (2023).
Alexander Koebler, Thomas Decker, Michael Lebacher, Ingo Thon,
Volker Tresp, and Florian Buettner. Towards Explanatory
Model Monitoring. In: NeurIPS Workshop: XAI in Action: Past,
Present, and Future Applications, 2023
Gengyuan Zhang, Jinhe Bi, Jindong Gu, and Volker Tresp. "SPOT!
Revisiting Video-Language Models for Event Understanding." arXiv
preprint arXiv:2311.12919 (2023).
Shuo Chen, Zhen Han, Bailan He, Mark Buckley, Philip Torr,
Volker Tresp, and Jindong Gu. "Understanding and Improving
In-Context Learning on Vision-language Models." arXiv preprint
arXiv:2311.18021 (2023).
Shuo Chen, Jindong Gu, Zhen Han, Yunpu Ma, Philip Torr, and
Volker Tresp. "Benchmarking robustness of adaptation methods on
pre-trained vision-language models." Advances in Neural
Information Processing Systems 36 (2023)
Zifeng, Ding, Bailan He, Yunpu Ma, Zhen Han, and Volker Tresp.
Learning Meta Representations of One-shot Relations for Temporal
Knowledge Graph Link Prediction. IJCNN, 2023.
Julia Gottfriedsen, Johanna Strebl, Dominik Laux, Franziska
Kraft, Martin Langer, and Volker Tresp. Developing regional,
data-driven short-term wildfire hazard models using deep
learning and xAI. AGU Fall Meeting, 2023
Zifeng Ding, Jingcheng Wu, Jingpei Wu, Yan Xia, and Volker
Tresp. Exploring Link Prediction over Hyper-Relational Temporal
Knowledge Graphs Enhanced with Time-Invariant Relational
Knowledge. arXiv preprint arXiv:2307.10219 (2023).
Zefeng Wang, Zhen Han, Shuo Chen, Volker Tresp, Jindong Gu.
Towards the Adversarial Robustness of Vision-Language Model with
Chain-of-Thought Reasoning, 2023.
Jindong Gu, Volker Tresp, Yao Qin. Evaluating model robustness
to patch perturbations. ICML 2022 Shift Happens Workshop,
2022.
Aneta Koleva, Martin Ringsquandl, and Volker Tresp. Analysis
of the Attention in Tabular Language Models. In NeurIPS 2022
First Table Representation Workshop. 2022.
Guo, Jin, Zhen Han, Zhou Su, Jiliang Li, Volker Tresp, and
Yuyi Wang. Continuous Temporal Graph Networks for Event-Based
Graph Data. arXiv:2205.15924, 2022.
Ute Schmid, Volker Tresp, Matthias Bethge, Kristian
Kersting, and Rainer Stiefelhagen. Künstliche
Intelligenz – Die dritte Welle. In: Reussner, R. H.,
Koziolek, A. & Heinrich, R. (eds), INFORMATIK 2020
- Jahrestagung der Gesellschaft für Informatik e.V., 2021.
[PDF]
Ahmed Frikha, Denis Krompass, and Volker Tresp. Columbus:
Automated discovery of new multi-level features for domain
generalization via knowledge corruption. arXiv preprint
arXiv:2109.04320, 2021.
M Alam, M Ali, P Groth, P Hitzler, J Lehmann, H Paulheim, A
Rettinger, H Sack, A Sadeghi, and Volker Tresp. MLSMKG 2021:
Machine Learning with Symbolic Methods and Knowledge Graphs,
co-located with ECML PKDD, 2021
Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp.
Temporal knowledge graph forecasting with neural ode. arXiv:2101.05151,
2021.
Rajat Koner, Poulami Sinhamahapatra, and Volker Tresp. Scenes
and surroundings: Scene graph generation using relation
transformer. arXiv:2107.05448, 2021.
Jindong Gu, Hengshuang Zhao, Volker Tresp, Philip Torr.
Adversarial examples on segmentation models can be easy to
transfer
Authors. arXiv:2111.11368, 2021.
Hang Li, Chengzhi Shen, Philip Torr, Volker Tresp, and Jindong
Gu. "Self-discovering interpretable diffusion latent directions
for responsible text-to-image generation." arXiv preprint
arXiv:2311.17216 (2023).
Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma,
Martin Ringsquandl, Mitchell Joblin, and Volker Tresp. Reasoning on Knowledge
Graphs with Debate Dynamics, AAAI Conference on
Artificial Intelligence (AAAI), 2020 [PDF]
Volker Tresp and Yunpu Ma. The Tensor Memory
Hypothesis. NIPS 2016 Workshop on Representation Learning
in Artificial and Biological Neural Networks (MLINI 2016), 2016.[PDF]
Daniel Sonntag, Volker Tresp, Sonja Zillner, Alexander
Cavallaro, Matthias Hammon, André Reis, Peter A Fasching, Martin
Sedlmayr, Thomas Ganslandt, Hans-Ulrich Prokosch, Klemens Budde,
Danilo Schmidt, Carl Hinrichs, Thomas Wittenberg, Philipp
Daumke, and Patricia G Oppelt. The
Clinical Data Intelligence Project. Informatik-Spektrum,
2016.[PDF]
Evrim Acar, Animashree Anandkumar, Lenore Mullin, Sebnem
Rusitschka, and Volker Tresp.
Tensor Computing for Internet of Things (Dagstuhl
Perspectives Workshop 16152). Dagstuhl Reports 6(4): 57-79
(2016)
2015
Volker Tresp, Cristóbal Esteban, Yinchong Yang, Stephan
Baier, and Denis Krompaß. Learning with Memory
Embeddings. NIPS 2015 Workshop on Nonparametric
Methods for Large Scale Representation Learning (extended TR), 2015. [PDF]
Denis Krompaß, Xueyan Jiang, Maximilian Nickel, and Volker
Tresp. Probabilistic
Latent-Factor Database Models. Proceedings of the ECML
workshop on Linked Data for Knowledge Discovery, 2014.[PDF]
Volker Tresp, Sonja Zillner, Maria J. Costa, Yi Huang,
Alexander Cavallaro, Peter A. Fasching, Andre Reis, Martin
Sedlmayr, Thomas Ganslandt, Klemens Budde, Carl Hinrichs, Danilo
Schmidt, Philipp Daumke, Daniel Sonntag, Thomas Wittenberg,
Patricia G. Oppelt, and Denis Krompass. Towards a New Science of
a Clinical Data Intelligence. NIPS 2013 Workshop on
Machine Learning for Clinical Data Analysis and Healthcare,
CoRR, arXiv:1311.4180 [cs.CY],2013.
Mohamed Yahya, Klaus Berberich, Shady Elbassuoni, Maya
Ramanath, Volker Tresp, and Gerhard Weikum. Natural
Language Questions for the Web of Data.Empirical Methods
in Natural Language Processing and Natural Language Learning
(EMNLP-CoNLL'12), 2012. [PDF}
Achim Rettinger, Matthias Nickles, and Volker Tresp. Statistical
relational learning with formal ontologies. In Proceedings
of The European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML PKDD),
2009. [PDF]
Volker Tresp, Yi Huang, Markus Bundschus, and Achim
Rettinger. Materializing
and querying learned knowledge. In Proceedings of
the First ESWC Workshop on Inductive Reasoning and Machine
Learning on the Semantic Web (IRMLeS 2009), 2009. [PDF]
Dieter Fensel, Frank van Harmelen, Bo Andersson, Paul
Brennan, Hamish Cunningham, Emanuele Della Valle, Florian
Fischer, Zhisheng Huang, Atanas Kiryakov, Tony Kyung
il Lee, Lael Schooler, Volker Tresp, Stefan Wesner, Michael
Witbrock, and Ning Zhong. Towards
larkc: A platform for web-scale reasoning. In Proceedings
of the 2th IEEE International Conference on Semantic Computing
(ICSC 2008), 2008.[PDF]
Achim Rettinger, Matthias Nickles, and Volker Tresp. A
statistical relational model for trust learning. In Proceeding
of 7th International Conference on Autonomous Agents and
Multiagent Systems (AAMAS 2008), 2008. [PDF]
Volker Tresp, Markus Bundschus, Achim Rettinger, and
Yi Huang. Towards
machine learning on the semantic web. In: Costa, Paulo C.
G.; D'Amato, Claudia; Fanizzi, Nicola; Laskey, Kathryn B.;
Laskey, Kenneth J.; Lukasiewicz, Thomas; Nickles, Matthias; and
Pool, Michael (Eds.): Uncertainty Reasoning for the Semantic Web
I Lecture Notes in AI, Springer, 2008. [PDF]
Achim Rettinger, Matthias Nickles, and Volker Tresp. Learning
initial trust among interacting agents. In Eleventh
International Workshop CIA 2007 on Cooperative Information
Agents. Springer 2007, September 2007. [PDF]
Zhao Xu, Volker Tresp, Kai Yu, and Hans-Peter Kriegel. Infinite
hidden relational models. In Proceedings of the
22nd International Conference on Uncertainty in Artificial
Intelligence (UAI 2006), 2006. [PDF]
Shipeng Yu, Kai Yu, and Volker Tresp. Collaborative
ordinal regression. In The 23nd International
Conference on Machine Learning (ICML 2006), 2006. [PDF]
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel, and
Mingrui Wu. Supervised
probabilistic principal component analysis. In 12th
ACM International Conference on Knowledge Discovery and Data
Mining (KDD 2006), 2006. [PDF]
2005
Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, and Hans-Peter
Kriegel. Dirichlet
enhanced relational learning. In The 22nd
International Conference on Machine Learning (ICML 2005),
2005. [PDF]
Kai Yu, Shipeng Yu, and Volker Tresp. Blockwise supervised
inference on large graphs. In Proceedings of
Workshop on Learning with Partially Classified Training Data
at the 22nd International Conference on Machine Learning (ICML
2005), 2005. [PDF]
Kai Yu, Shipeng Yu, and Volker Tresp.
Multi-output regularized projection. In IEEE
Computer Society International Conference on Computer Vision
and Pattern Recognition (CVPR 2005), 2005.
[PDF]
Yi
Huang, Kai Yu, Matthias Schubert, Shipeng Yu, Volker Tresp, and Hans-Peter
Kriegel. Hierarchy-Regularized
Latent Semantic Indexing.IEEE
International Conference on Data Mining - ICDM, 2005. [PDF]
Volker Tresp. Committee
machines. In Yu Hen Hu and Jenq-Nen Hwang, editors, Handbook
for Neural Network Signal Processing. CRC Press, 2001.
[PDF]
Volker Tresp.
Scaling kernel-based systems to large data sets. Data
Mining and Knowledge Discovery, 5, Special issue on Statistical
Models for Data Mining, edited by Paolo Giudici, David
Heckerman, Joe Whittaker, 2001. [PDF]
Thomas Briegel and Volker Tresp. Dynamic neural regression models.
Technical report, Instituts für Statistik der
Ludwig-Maximilians-Universität München, 2000. Discussion Paper
181. [PDF]
Volker Tresp, Michael Haft, and Reimar Hofmann. Mixture
approximations to bayesian networks. In K. B. Laskey
and H. Prade, editors, Uncertainty in Artificial
Intelligence, Proceedings of the Fifteenth Conference.
Morgan Kaufmann Publishers, 1999. [PDF]
Harald Steck and Volker Tresp. Bayesian
Belief Networks for Data Mining. In: Proceedings des 2. Workshops
über Data Mining und Data Warehousing als Grundlage moderner
entscheidungsunterstützender Systeme. Eds.: Univ. Magdeburg.,
1999[PDF]
Reimar Hofmann and Volker Tresp. Nonlinear
markov networks for continuous variables. In M. I.
Jordan, M. S. Kearns, and S. A. Solla, editors, Advances
in Neural Information Processing Systems (NIPS*1997).
MIT Press, 1997. [PDF]
Volker Tresp, Ralph Neuneier, and Hans-Georg Zimmermann. Early
brain damage. In M. Mozer, M. I. Jordan, and
T. Petsche, editors, Advances in Neural Information
Processing Systems (NIPS*1996). MIT Press, 1996. [PDF]
Volker Tresp, Subutai Ahmad, and Ralph Neuneier. Training
neural networks with deficient data. In J. D. Cowan,
G. Tesauro, and J. Alspector, editors, Advances
in Neural Information Processing Systems (NIPS*1993).
Morgan Kaufmann, 1993. [PDF]
Volker Tresp, Jürgen Hollatz, and Subutai Ahmad. Network
structuring and training using rule-based knowledge. In
C. L. Giles, Hanson S. J., and Cowan J. D.,
editors, Advances in Neural Information Processing
Systems (NIPS*1992). Morgan Kaufman, 1992. [PDF]
Volker Tresp, Ira Leuthäusser, Martin Schlang, Ralph Neuneier,
Klaus Abraham-Fuchs, and Wolfgang Härer. The neural impulse
response filter. In International Conference on
Artificial Neural Networks II. North Holland,
1992. [PDF]
Jürgen Hollatz, Volker Tresp: Integrating Rule-Based Knowledge
into Neural Computing.
DAGM-Symposium, 1992.
Martin F. Schlang,Volker Tresp,Klaus Abraham-Fuchs,Wolfgang Härer, and P. Weismüller. Neuronale Netze zur Segmentierung und Clusterung
von biomagnetischen Signalen.DAGM-Symposium,
1992.
"Bad talks make you want to die
and good talks mess up your brain" (why one should avoid
talks) more
Origin of the name Tresp (my
best guess). Tresp is related to the Saxon (mittelniederdeutsch)
word "dreist", which means audacious. The name would really
stand for someone who comes from the village where one can
cross over the bubbling ("dreisten") brook. The brook is called
Dreisbach, the village Drespe (earlier form: Dreispe). The term is
related to the Celtic term for “bubbling spring”. Then the "e"
was droppped and in East Prussia, to where some people of that name
had immigrated (first records around 1650), the "D" changed to a
"T".
Then there is also: trespe, f. , ein unter dem getreide wachsendes
unkraut (Deutsches Wörterbuch von Jacob Grimm und Wilhelm Grimm)
and Triesch (Brache: unused farmland)
Scientific
Genealogy shows that
my academic ancestors are three Nobel prize winners (W. K.
Heisenberg, P. Debye, F. Bloch). Via advisors and co-advisors, my
academic lineage goes back to C. F. Gauß, and G. W.
Leibniz. Around 1983, my diploma co-advisors (Udo Kaatze)
organized a workshop together with Erwin Neher
who later wrote a recommendation letter for me for applying for a
stipend to attend a university in the U.S. In 1991,
Neher was awarded, along with Bert Sakmann, the Nobel Prize in
Physiology or Medicine for "their discoveries concerning the
function of single ion channels in cells" (
thepatch-clamp technique).
Around 1985, Eric Kandel
came to our flat after his talk at Yale discussing
neuroscience. Kandel won the 2000 Nobel Prize in Physiology
or Medicine for his research on the physiological basis of memory
storage in neurons. Reinhard Pottel and Udo Kaatze were my diploma
thesis advisors. My diploma thesis was a follow-up project of the
amazing PhD project of Eberhard Asselborn. In addition to physics,
Eberhard studied medicine and became an Ophthalmologist. His
reasoning for turning to medicine was that he did not think his
purpose in life was to build smart refrigerators. My PhD advisors
were Art Gmitro and Gene Gindi.
