Volker Tresp

News

• Invited presentation at Imperial College London in the series "Imperial AI Talks"
  
• QLindA (Quantum Reinforcement Learning für industrielle Anwendungen) has started. QLindA 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
  
• Co-author of the Best Paper Runner-up at SSDBM 2021 (with Julian Busch, Anton Kocheturov, and Thomas Seidl)
  
• On June 7 we will have our official kickoff workshop. ELLIS (https://ellis.eu/) programm on "Semantic Symbolic, and Interpretable Machine Learning". All the keynotes will be broadcasted via YouTube. The YouTube stream will be visible on https://ellis.eu/events/ellis-program-semantic-symbolic-and-interpretable-machine-learning-kick-off
• PI in ELISE (European Learning and Intelligent Systems Excellence), a European Network of AI Excellence Centres, funded under EU Horizon 2020 (2020-2023)
• My ELLIS programm on Semantic, Symbolic and Interpretable Machine Learning is approved; I became an ELLIS Fellow
  
• Co-author of the Best Paper Award, IEEE ICHI 2020  (with Zhiliang Wu, Yinchong Yang, Yunpu Ma, Yushan Liu, Rui Zhao, Michael Moor)
• Oral presentation at the Cold Spring Harbor Laboratory Meeting:  From Neuroscience to Artificially Intelligent Systems (NAISys) 
  
• PyKEEN is our new PyTorch-based library for knowledge graph embeddings (Project Page, Publication). PyKEEN evolved out off a collaboration between the LMU, Uni Bonn and TU Denmark. PyKEEN permits a comparative evaluation of different embedding approaches
• My team is a partner in ELISE, an EU2020 project that specifically focuses on fundamental research in AI, driven by machine learning (ML)
  
• I am a co-organizer of the CIKM workshop Combining Symbolic And Sub-Symbolic Methods And Their Applications (CSSA), 2020
• with Kristian Kersting: Maschinelles und Tiefes Lernen Der Motor für „KI made in Germany“   White Paper der AG 1: Technologische Wegbereiter und Data Science der Plattform Lernende Systeme [PDF]
  
• EU ITN MARIE CURIE ACTIONS on Machine Learning Frontiers in Precision Medicine (MLFPM) (2018-2022)
  
• Coordinator of the BMBF funded project Machine Learning with Knowledge Graphs (MLwin - Maschinelles Lernen mit Wissensgraphen) 2018-2021
• PI in the Munich Center for Machine Learning (MCML) 2018-2022

Yesterday's News

• Co-organizer of the ECML PKDD Workshop 2019 "New Trends in Representation Learning with Knowledge Graphs"
  
• Keynote at Machine Learning and Applications Summer School
• Invited to the Enquete-Kommission Künstliche Intelligenz of the "Deutschen Bundestag" to talk about industrial AI
• Invited keynote at the ESWC 2019 Workshop on Deep Learning For Knowledge Graphs (June 2nd, 2019)
  
• Speaker at the workshop on Low-rank Optimization and Applications organized by the Max Planck Institute for Mathematics in the Sciences (April 01 - 05, 2019)
• Invited keynote speaker at: Quantum Machine Learning & Biomimetic Quantum Technologies 
  Co-author of the Student Best Paper Award, ISWC 2017 (with Stephan Baier und Yunpu Ma)
  
• Coordinator of the BMWi funded project Klinische Datenintelligenz (Clinical Data Intelligence); final review meeting in Sept 2017 was a great success and made our sponsors happy
  
• Cognitive Deep Learning project start. Cognitive Deep Learning bridges the gap between Deep Learning (Convolutional Neural Networks, Recurrent Neural Networks, Representation Lerning, Tensor Modelling) and Cognitive Neuroscience
• With financial support of Siemens the Data Science Lab started operations at the Ludwig Maximilian University of Munich
• Coordinator of the Siemens-LMU activities in the  Campus AD
• Pictures of the Future
  
• Guest Editor of the Proceedings of the IEEE for a Special Issue on Big Data in Use 
• Invited talk at  the Workshop Deep Learning: Theory, Algorithms and Applications  at MIT's McGovern Institute for Brain ResearchTalks in 2016 at ETH, MIT, Yale, UMass, Oxford and  Cambridge

Research Interests

• Machine Learning and Deep Learning
  
• Cognitive Deep Learning
  
• Machine Learning with the Semantic Web, Linked Data, and Knowledge Graphs
  
• Statistical Relational Learning
• Tensor decompositions and multiway Neural Networks
  
• Neural Networks,  Pattern Recognition
• Temporal Models
  
• Infinite Models: Gaussian Processes, Dirichlet Processes
  
• Graphical Models, Bayesian Networks
• User Modeling
• Computational Cognition and Cognitive Neuroscience
  
• Bioinformatics
  
• Information Extraction, Information Retrieval
• Medical Decision Support Systems, Precision Medicine
• Reinforcement Learning and Multi-Agent Systems

Biography

Volker Tresp is a professor at Ludwig Maximilian University of Munich (LMU) and Distinguished Research Scientist at Siemens Research. He received his Diploma degree in physics from the University of Göttingen in 1984 and M.Sc., M.Phil. and Ph.D. degrees from Yale University in 1986 and 1989, respectively. During his Ph.D., he worked in Yale’s Image Processing and Analysis Group (IPAG). In 1990, he joined Siemens where he has been heading various research teams in machine learning. In 1994 he was a visiting scientist at the Massachusetts Institute of Technology in the Center for Biological and Computational Learning, working with the teams of Tomaso Poggio and Michael I. Jordan. He was co-editor of Advances 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 the Bayesian Committee Machine and his work on hierarchical learning with Gaussian processes. The IHRM, the SRM,  SUNS, and RESCAL are 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 the Tensor Brain reflects his interest in mathematical models for cognition and neuroscience. In 2020, he became a Fellow of the European 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".

Students

• Yize Sun, Ludwig Maximilian University of Munich
• Shuo Chen, Ludwig Maximilian University of Munich
• Bailan He, Ludwig Maximilian University of Munich
• Ruotong Liao, Ludwig Maximilian University of Munich-VL
• Haokun Chen, Ludwig Maximilian University of Munich
• Yao Zhang, Ludwig Maximilian University of Munich-VL
• Gengyuan Zhang, Ludwig Maximilian University of Munich-VL
• Zifeng Ding, Ludwig Maximilian University of Munich-VS
• Thomas Decker, Ludwig Maximilian University of Munich
• Aneta Koleva, Ludwig Maximilian University of Munich
• Hang Li, Ludwig Maximilian University of Munich-VS
• Yushan Liu, Ludwig Maximilian University of Munich
• Rajat Koner, Ludwig Maximilian University of Munich-VL
• Sören-Jannik Nolting, Ludwig Maximilian University of Munich
• Julia Gottfriedsen, Ludwig Maximilian University of Munich
• Ilja Manakov, Ludwig Maximilian University of Munich
• Markus Rohm, Ludwig Maximilian University of Munich

Former Ph.D. Students

• Sahand Sharifzadeh, (LMU, 2023)
• Ahmed Frikha (LMU, 2022)
  
• Zhen Han (LMU, 2022)
• Jindong Gu (LMU, 2022)
  
• Max Berrendorf (LMU, 2022)
  
• Zhiliang Wu (LMU, 2022)
  
• Marcel Hildebrandt (LMU, 2021)
  
• Rui Zhao (LMU, 2020)
  
• Yunpu Ma (LMU, 2020)
  
• Stephan Baier (LMU, 2019)
  
• Yinchong Yang (LMU, 2018)
  
• Cristóbal Esteban (LMU, 2018)
• Jonathan Boidol, (LMU 2017)
  
• Denis Krompaß (LMU, 2015)
  
• Xueyan Jiang (LMU, 2014)
  
• Maximilian Nickel (LMU, 2013)
  
• Yi Huang (LMU, 2020)
  
• Achim Rettinger (TUM, 2010)
  
• Markus Bundschus (LMU, 2010)
  
• Zhao Xu (LMU, 2007)
  
• Shipeng Yu (LMU, 2006)
  
• Kai Yu (LMU, 2004)
  
• Anton Schwaighofer (U of Graz, 2003)
  
• Harald Steck (TUM, 2001)
  
• Jaakko Hollmen (Helsinki U. of T., 2000)
• Thomas Briegel (TUM, 1999)
• Dirk Ormoneit (TUM, 1999)
• Ralph Neuneier (T.U. of Kaiserslautern, 1998)
• Reimar Hofmann (TUM, 1997)
  
• Jürgen Hollatz (TUM, 1992)

Selected Former Team Members and Collaborators

• Rime Raissouni (2021)
  
• Laurent Vermue (2019)
  
• Erik Daxberger (2018)
• Pasquale Minervini (2013)
• Mathaeus Dejori (2010-2012)
  
• Joshua L. Moore (2011)
  
• Florian Steinke (2010)
  
• Christoph Lippert (2008)
• Derrick Pisani (2003)
• Michael Haft (1999-2003)
• Oliver Mihatsch (1996-2003)
  
• Thomas Zahn (1991)
• Martin Roscheisen (1991)

More Friends that Crossed by Path

• Paolo Giudici
• Cesare Alippi

Companies Founded by Former Team Members

• 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)
  
• Siemens Inventor of the Year for 1996
  

Tutorials

• Maximilian Nickel and Volker Tresp. Machine Learning on Linked Data: Tensors and their Applications in Graph-Structured Domains. Tutorial at the ISWC 2012.
• Yi Huang and Volker Tresp. Accessing the Semantic Web via Statistical Machine Learning. Tutorial at the ESWC 2012.
  
• Volker Tresp. Multivariate Models for Relational Learning. Tutorial at the ILP 2010.
  
• Volker Tresp and Kai Yu. Learning with dependencies between several response variables: From hierarchical bayes and multitask learning to structured prediction and relational learning. Tutorial at the ICML 2009. [PDF]
• Volker Tresp. Dirichlet processes and nonparametric bayesian modelling. Tutorial at the Machine Learning Summer School 2006 in Canberrra, Australia. Video.
  

Software

• PyKEEN is our new PyTorch-based library for knowledge graph embeddings (Project Page, Publication). PyKEEN evolved out off a collaboration between the LMU, Uni Bonn and TU Denmark. PyKEEN permits a comparative evaluation of different embedding approaches.
• The RESCAL code can be downloaded at Maximilian's homepage. GitHub code for RESCAL extensions (SGD, ...) and other Knowledge Graph learning algorithms
  
• The RESCAL code with type constraints  and code for non-negative RESCAL. See also  Denis' homepage 
  

Books

• with Todd K. Leen and Thomas G. Dietterich:  The proceedings of the 2000 Neural Information Processing Systems (NIPS) Conference, MIT Press 2001,  ISBN  978-0262526517
  

Papers

• Volker Tresp, Sahand Sharifzadeh,  Hang Li,  Dario Konopatzki, and  Yunpu Ma. The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding. Neural Computation, 2023.[PDF]
• Rajat Koner, Tanveer Hannan, Suprosanna Shit, Sahand Sharifzadeh, Matthias Schubert, Thomas Seidl, and Volker Tresp. InstanceFormer: An Online Video Instance Segmentation Framework. AAAI, 2023.
  

• Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin, Volker Tresp, and Benjamin M. Gyori. A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs. Workshop on Graph Learning Benchmarks. The WebConf,  2022. [PDF]
  
• Ahmed Frikha, Denis Krompaß, and Volker Tresp. Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption. ICPR, 2022. [PDF]
  
• Aneta Koleva, Martin Ringsquandl, Mark Buckley, Rakebul Hasan, and Volker Tresp. Named Entity Recognition in Industrial Tables using Tabular Language Models. EMNLP,  2022. [PDF]
  
• Jindong Gu, Hengshuang Zhao, Volker Tresp, and Philip Torr. SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness. ECCV, 2022. [PDF]
  
• Jindong Gu, Volker  Tresp, and Yao Qin. Are vision transformers robust to patch perturbations? ECCV, 2022. [PDF]
  
• Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma, Zhen Han, and Volker Tresp. Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information. AKBC, 2022. [PDF]
  
• Guirong Fu,  Zhao Meng, Zhen Han, Zifeng Ding, Yunpu Ma, Matthias Schubert, Volker Tresp, and Roger Wattenhofer. TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion. Proceedings of the Sixth Workshop on Structured Prediction for NLP, 2022. [PDF]
  
• Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp and  Bjoern Menze. Relationformer: A Unified Framework for Image-to-Graph Generation. ECCV, 2022. [PDF]
  
• Hang Li, Qadeer Khan, Volker Tresp, and Daniel Cremers. Biologically Inspired Neural Path Finding. International Conference on Brain Informatics, 2022. [PDF]
  
• Tong Liu, Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Hang Li, and Volker Tresp. On Calibration of Graph Neural Networks for Node Classification. IJCNN (IEEE WCCI), 2022.  [PDF]
  
• Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, and Volker Tresp. TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs. AAAI, 2022. [PDF]
• Sahand Sharifzadeh, Sina Moayed Baharlou, Martin Schmitt, Hinrich Schütze, and Volker Tresp. Improving Scene Graph Classification by Exploiting Knowledge from Texts. AAAI, 2022. [PDF]
  

• Zhen Han, Gengyuan Zhang, Yunpu Ma, and Volker Tresp. Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework.  Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021 [PDF]
  
• Max Berrendorf, Evgeniy Faerman, and Volker Tresp. Active Learning for Entity Alignment. 43rd European Conference on IR Research (ECIR), 2021. [PDF]
  
• Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp. OODformer: Out-Of-Distribution Detection Transformer. The British Machine Vision Conference (BMVC),  2021. [PDF]
  
• Malte Feucht, Zhiliang Wu, Sophia Althammer, and Volker Tresp. Description-based Label Attention Classifier for Explainable ICD-9 Classification.
  Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT at EMNLP),  2021 [PDF]
  
• Yunpu Ma and Volker Tresp.  Quantum Machine Learning Algorithm for Knowledge Graphs. ACM Transactions on Quantum Computing,  Vol. 2, No. 3, 2021. [PDF]
  
• Jindong Gu, Rui Zhao, and Volker Tresp. Semantics for Global and Local Interpretation of Deep Convolutional Neural Networks.  International Joint Conference on Neural Networks (IJCNN), 2021. [PDF]
  
• Aneta Koleva, Martin Ringsquandl, Mitchell Joblin and Volker Tresp.  Generating Table Vector Representations. ISWC Workshop DL4KG, 2021. [PDF]
  
• Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, and Jens Lehmann.
  Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models under a Unified Framework. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE PAMI), 2021 [PDF]
  
• Zhen Han,  Zifeng Ding, Yunpu Ma,Yujia Gu, and Volker Tresp. Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs. EMNLP, 2021.[PDF]
  
• Zhiliang Wu, Yinchong Yang, Jindong Gu, and Volker Tresp.  Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning.  Proceedings of the  IEEE International Conference on Healthcare Informatics, ICHI, 2021.[PDF]
• Mehdi Ali*, Max Berrendorf*, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, and Jens Lehmann. Improving Inductive Link Prediction Using Hyper-Relational Facts.  ISWC (best research paper award), 2021.[PDF]
• Rajat Koner*, Hang Li*,  Marcel Hildebrandt*, Deepan Das, Volker Tresp, and Stephan Guennemann. Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering. ISWC, 2021. [PDF]
  
• Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Martin Ringsquandl, Rime Raissouni, and Volker Tresp. Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs. ESWC, 2021. [PDF]
  
• Mehdi Ali*, Max Berrendorf*, Charles Tapley Hoyt*,  Laurent Vermue*, Sahand Sharifzadeh, Volker Tresp, and Jens Lehmann. PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings. JMLR, 22(82):1−6, 2021 [PDF]
  
• Yunpu Ma and Volker Tresp. Causal Inference under Networked Interference and Intervention Policy Enhancement. International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 [PDF]
  
• Zhen Han,   Peng Chen, Yunpu Ma, and Volker Tresp. Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs, ICLR, 2021.[PDF]
• Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp,  and Wei Xu. Mutual Information State Intrinsic Control. ICLR, 2021.[PDF]
• Jindong Gu, Baoyuan Wu, Volker Tresp. Effective and Efficient Vote Attack on Capsule Networks, ICLR, 2021.[PDF]
  
• Sahand Sharifzadeh, Sina Moayed Baharlou, and Volker Tresp.  Classification by Attention: Scene Graph Classification with Prior Knowledge, AAAI, 2021. [PDF]
  
• Ahmed Frikha, Denis Krompaß, Hans-Georg Köpken, and Volker Tresp. Few-Shot One-Class Classification via Meta-Learning, AAAI,  2021. [PDF]
  
• Jindong Gu and  Volker Tresp.  Capsule Network is Not More Robust than Convolutional Network. CVPR, 2021. [PDF]
  
• Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl. NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification, SSDBM (best paper runner-up ), 2021 [PDF]
  
• Zhiliang Wu,  Yinchong Yang,  Peter A. Fasching, and Volker Tresp.  Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models. Machine Learning for Healthcare (MLHC) & Proceedings of Machine Learning Research (PMLR), 2021 [PDF]
  
• 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]
  

• Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp.  DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion. Proc. of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP),  2020 [PDF]
  
• Martin Schmitt, Sahand Sharifzadeh, Volker Tresp, and Hinrich Schütze. An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing. Proc. of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP),  2020 [PDF]
  
• Jindong Gu and Volker Tresp. Improving the Robustness of Capsule Networks to Image Affine Transformations. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 [PDF]
  
• 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]
• Zhiliang Wu, Yinchong Yang, Yunpu Ma, Yushan Liu, Rui Zhao, Michael Moor, and Volker Tresp.  Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score. Proc. of the  IEEE International Conference on Healthcare Informatics  (ICHI), 2020 [PDF]
  
• Zhen Han,  Yunpu Ma, Yuyi Wang, Stephan Günnemann, and Volker Tresp. Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs (best paper nominee). Automated Knowledge Base Construction (AKBC),  2020 [PDF]
  
• Sahand Sharifzadeh, Sina Baharlou, Max Berrendorf, Rajat Koner, and Volker Tresp. Improving Visual Relation Detection using Depth Maps. International Conference on Pattern Recognition (ICPR), 2020 [PDF]
  
• Ahmed Frikha, Denis Krompass, and Volker Tresp. ARCADe: A Rapid Continual Anomaly Detector. International Conference on Pattern Recognition (ICPR), 2020 [PDF]
  
• Jindong Gu and  Volker Tresp.  Search for Better Students to Learn Distilled Knowledge. European Conference on Artificial Intelligence (ECAI), 2020 [PDF]
  
• Mahdyar Ravanbakhsh,  Vadim Tschernezki, Felix Last, Tassilo Klein,  Kayhan Batmanghelich,  Volker Tresp, and Moin Nabi. Human-Machine Collaboration for Medical Image Segmentation. IEEE ICASSP,  2020 [PDF]
• Feifei Xu, Xinpeng Wang, Yunpu Ma, Volker Tresp, Yuyi Wang, Shanlin Zhou, and Haizhou Du. Controllable Multi-Character Psychology-Oriented Story Generation. Proc. of the 29th ACM International Conference on Information & Knowledge Management (CIKM), 2020 [PDF]
  
• Mohammad KaramiNejadRanjbar, Sahand Sharifzadeh, Nina C. Wietek, Mara Artibani, Salma El-Sahhar, Tatjana Sauka-Spengler, Christopher Yau, Volker Tresp, and Ahmed Ahmed.  A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes.   Elife 9, 2020 [PDF]
• Dietrich Trautmann, Michael Fromm, Volker Tresp, Thomas Seidl, and Hinrich Schütze. Relational and Fine-Grained Argument Mining, Datenbank-Spektrum, 2020 [PDF]
• Heiko Paulheim, Volker Tresp, and Zhiyuan Liu. Representation Learning for the Semantic Web. Journal of Web Semantics, 2020
• Jindong Gu, Zhiliang Wu, and Volker Tresp. Introspective Learning by Distilling Knowledge from Online Self-explanation. Proceedings of the Asian Conference on Computer Vision, 2020 [PDF]
  
• Max Berrendorf, Evgeniy FaermanValentyn,  Melnychuk Volker Tresp, and Thomas Seidl. Knowledge graph entity alignment with graph convolutional networks: Lessons learned. European Conference on Information Retrieval, ECIR, 2020 [PDF]
  

• Marcel Hildebrandt, Mohamed Khalil, Christoph Bergs, Volker Tresp, Roland Wüchner, Kai-Uwe Bletzinger, and Michael Heizmann. Remaining Useful Life Estimation for Unknown Motors Using a Hybrid Modeling Approach. IEEE International Conference on Industrial Informatics (INDIN), 2019 [PDF]
  
• Volker Tresp, Sahand Sharifzadeh, and Dario Konopatzki. The Tensor Brain Hypothesis. Computational Cognition (ComCo), 2019
  
• Volker Tresp, Sahand Sharifzadeh, and Dario Konopatzki. A Model for Perception and Memory. Conference on Cognitive Computational Neuroscience (CCN), 2019 [PDF]   
• Kristian Kersting, and Volker Tresp.  Maschinelles und Tiefes Lernen Der Motor für „KI made in Germany“.   White Paper der AG 1: Technologische Wegbereiter und Data Science der Plattform Lernende Systeme [PDF]
• Yinchong Yang, Zhiliang Wu, Volker Tresp, and Peter Fasching. Categorical EHR Imputation with Generative Adversarial Nets. Proceedings of the  IEEE International Conference on Healthcare Informatics  (ICHI), 2019.[PDF]
• Rui Zhao, Xudong Sun, and Volker Tresp.  Maximum Entropy-Regularized Multi-Goal Reinforcement Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97:7553-7562, 2019.[PDF]
  
• Marcel Hildebrandt, Swathi Shyam Sunder, Serghei Mogoreanu, Mitchell Joblin, Akhil Mehta, Ingo Thon, and Volker Tresp. A Recommender System for Complex Real-World Applications with Nonlinear Dependencies and Knowledge Graph Context, Extended Semantic Web Conference (ESWC),  2019. [PDF]
  
• Yunpu Ma, Volker Tresp, Liming Zhao, and Yuyi Wang. Variational Quantum Circuit Model for Knowledge Graphs Embedding. Advanced Quantum Technologies, 2019.[PDF]
• Ilja Manakov, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, and Volker Tresp. Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation. In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data,  MICCAI Workshop, 2019. [PDF]
• Christoph Bergs, Mohamed Khalil, Marcel Hildebrandt, Michael Heizmann, Roland Wüchner, Kai-Uwe Bletzinger, and Volker Tresp. Health indication of electric motors using a hybrid modeling approach. tm-Technisches Messen 86, 2019
  

• Yunpu Ma,  Marcel Hildebrandt, Stephan Baier,  and  Volker Tresp. Holistic Representations for Memorization and Inference. In Proceedings of the 34th International Conference on Uncertainty in Artificial Intelligence (UAI 2018). [PDF]
• Jindong Gu, Yinchung Yang, and Volker Tresp.  Understanding Individual Decisions of CNNs via Contrastive Backpropagation. Asian Conference on Computer Vision (ACCV), 2018.[PDF]
  
• Marcel Hildebrandt, Swathi Shyam Sunder, Serghei Mogoreanu, Ingo Thon, Volker Tresp, and Thomas Runkler. Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems. ECML-PKDD, 2018. [PDF]
• Yunpu Ma, Volker Tresp, Erik Daxberger.  Embedding Models for Episodic Knowledge Graphs. Journal of Web Semantics.  arXiv:1807.00228 [cs.AI],  2018 [PDF]
  
• Stephan Baier, Yunpu Ma,  and  Volker Tresp. Improving Information Extraction from Images with Learned Semantic Models. IJCAI (Sister Conferences Best Papers), 2018 [PDF]
  
• Yinchong Yang, Volker Tresp,  Marius Wunderle, and Peter A Fasching. Explaining Therapy Predictions with Layer-wise Relevance Propagation in Neural Networks. Proceedings of the  IEEE International Conference on Healthcare Informatics  (ICHI), 2018.[PDF] 
• Markus Rohm, Volker Tresp, Michael Müller, Christoph Kern, Ilja Manakov, Maximilian Weiss, Dawn A. Sim, Siegfried Priglinger, Pearse A. Keane,  and Karsten Kortuem. Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration. Ophthalmology, 2018.
• Rui Zhao and Volker Tresp.  Improving Goal-oriented Visual Dialog Agents via Advanced Recurrent Nets with Tempered Policy Gradient. Linguistic and Cognitive Approaches To Dialog Agents Workshop, 2018 [PDF]
• Rui Zhao and Volker Tresp. Energy-Based Hindsight Experience Prioritization. Conference on Robot Learning (CoRL). Proceedings of  Machine Learning Research Volume 87, 2018 [PDF]
• Rui Zhao and Volker Tresp. Learning Goal-oriented Visual Dialog via Tempered Policy Gradient. IEEE Spoken Language Technology  (SLT). IEEE, 2018.[PDF]
  
• Rui Zhao and Volker Tresp. Efficient Dialog Policy Learning via Positive Memory Retention. IEEE Spoken Language Technology  (SLT). IEEE, 2018.[PDF]
• Rui Zhao and Volker Tresp. Curiosity-Driven Experience Prioritization via Density Estimation. NeurIPS Deep RL Workshop,  2018. [PDF]

• Yinchong Yang, Volker Tresp,  and Peter A Fasching. Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Model. Proceedings of Machine Learning for Healthcare 2017 & JMLR W&C Track Volume 68, 2017.[PDF] 
  
• Yinchong Yang, Volker Tresp,  and Peter A Fasching.  Predictive Modeling of Therapy Decisions in Metastatic Breast Cancer with Recurrent Neural Network Encoder and Multinomial Hierarchical Regression Decoder. Proceedings of the  IEEE International Conference on Healthcare Informatics  (ICHI), 2017.[PDF]
• Volker Tresp, Yunpu Ma, Stephan Baier. Tensor Memories. Conference on Cognitive Computational Neuroscience. CCN,  2017.[PDF]
  
• Stephan Baier, Yunpu Ma,  and  Volker Tresp. Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions (best student paper award). ISWC, 2017.[PDF]
• Volker Tresp, Yunpu Ma, and Stephan Baier.  Learning with Knowledge Graphs.  Workshop on Neural-Symbolic Learning and Reasoning, 2017.[PDF]
• Yinchong Yang, Denis Krompass,  and Volker Tresp.  Tensor-Train Recurrent Neural Networks for Video Classification.  ICML & JMLR W&C Track Volume 70, 2017.[PDF]
• Volker Tresp, Yunpu Ma, Stephan Baier, and Yinchong Yang.  Embedding Learning for Declarative Memories.  ESWC Proceedings, 2017.[PDF]
• Stephan Baier, Sigurd Spieckermann, and Volker Tresp. Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks. ESANN, 2017.[PDF]
• Stephan Baier, Sigurd Spieckermann,  and Volker Tresp. Tensor Decompositions for Modeling Inverse Dynamics. Proceedings of the Congress of the International Federation of Automatic Control (IFAC),  2017.[PDF]
• Pasquale  Minervini, Volker Tresp, Claudia d’Amato, and Nicola Fanizzi.  Adaptive knowledge propagation in web ontologies.  ACM Transactions on the Web (TWEB), 2017.

• 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]
• Pasquale  Minervini, Claudia d’Amato, Nicola Fanizzi, and Volker Tresp.  Discovering Similarity and Dissimilarity Relations for Knowledge Propagation in Web Ontologies.  Journal on Data Semantics, 2016.
• Yinchong Yang, Peter A Fasching, Markus Wallwiener, Tanja N Fehm, Sara Y Brucker, Volker Tresp. Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding. NIPS'16 Workshop.  [PDF]
• Stephan Baier and Volker Tresp. Factorizing Sparse Tensors For Supervised Machine Learning. Tensor-Learn Workshop @ NIPS'16. 2016.
  
• Volker Tresp and Maximilian Nickel. Relational Models. Draft of a paper in the 2nd edition of the  Encyclopedia of Social Network Analysis and Mining, Springer, 2016. [PDF]
• Stephan Baier, Denis Krompass and Volker Tresp. Learning Representations for Discrete Sensor Networks using Tensor Decompositions (best paper nominee). IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2016. [PDF]
• Cristóbal Esteban, Oliver Staeck, Yinchong Yang, and Volker Tresp. Predicting Clinical Events by Combining Static and Dynamic Information using Recurrent Neural Networks (best paper nominee). Proceedings of the  IEEE International Conference on Healthcare Informatics  (ICHI), 2016. [PDF]
  
• Volker Tresp, J. Marc Overhage, Markus Bundschus, Shahrooz Rabizadeh, Peter Fasching, and Shipeng Yu. Going Digital: A Survey on Digitalization and  Large Scale Data Analytics in Healthcare, Proceedings of the IEEE, (invited paper), Special Issue on Big Data: Practical Applications, 2016. [PDF]
• Simon Haykin, Volker Tresp, and Jon Atli Benediktsson (Guest Editors). Scanning The Issue: Big Data: Practical Applications, Proceedings of the IEEE, 2016. [PDF]
• Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, and Denis Krompaß. Predicting the Co-Evolution of Event and Knowledge Graphs. International Conference on Information Fusion (FUSION), 2016. [TR 2015] [PDF]
  
• Yinchong Yang, Cristóbal Esteban and Volker Tresp. Embedding Mapping Approaches for Tensor Factorization and Knowledge Graph Modelling. In: The Semantic Web. Latest Advances and New Domains (ESWC Proceedings), 2016. [PDF]
  
• Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction.  Proceedings of the IEEE, (invited paper), 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]
• Bettina Berendt, Björn Bringmann, Élisa Fromont, Gemma C. Garriga, Pauli Miettinen, Nikolaj Tatti, and Volker Tresp. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings, Part III. Lecture Notes in Computer Science 9853, Springer 2016.
  
• 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)
  

• 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]
  
• Cristóbal Esteban, Danilo Schmidt, Denis Krompaß, and Volker Tresp. Predicting Sequences of Clinical Events by using a Personalized Temporal Latent Embedding Model. Proceedings of the  IEEE International Conference on Healthcare Informatics  (ICHI), 2015. [PDF]
  
• Denis Krompaß, Stephan Baier, and Volker Tresp. Type-Constrained Representation Learning in Knowledge Graphs.  Proceedings of the ISWC, 2015. [PDF]
  
• Denis Krompaß, Cristóbal Esteban, Volker Tresp, Martin Sedlmayr and Thomas Ganslandt. Exploiting Latent Embeddings of Nominal Clinical Data for Predicting Hospital Readmission. KI-Künstliche Intelligenz,  2015. [PDF]
• Denis Krompaß and Volker Tresp. Ensemble Solutions for Link-Prediction in Knowledge Graphs. ECML Workshop on Linked Data for Knowledge Discovery, 2015. [PDF]
  
• Jonathan Boidol, Andreas Hapfelmeier, and Volker Tresp. Probabilistic Hoeffding Trees - Sped-Up Convergence and Adaption of Online Trees on Changing Data Streams. Industrial Conference on Data Mining, 2015
  

• Maximilian Nickel*, Xueyan Jiang*, and Volker Tresp. Reducing the Rank of Relational Factorization Models by Including Observable Patterns (Learning from Latent and Observable Patterns in Multi-Relational Data). In Advances in Neural Information Processing Systems (NIPS*2014), 2014. [PDF] [Supplementary Material]
  
• Denis Krompaß, Maximilian Nickel, and Volker Tresp. Querying Factorized Probabilistic Triple Databases.  Proceedings of the ISWC, 2014 (best research paper nominee).[PDF]
• Denis Krompaß, Maximilian Nickel, and Volker Tresp. Large-Scale Factorization of Type-Constrained Multi-Relational Data.  International Conference on Data Science and Advanced Analytics (DSAA’2014), 2014.[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]
• Pasquale Minervini, Claudia D'Amato, Nicola Fanizzi and Volker Tresp. Learning to Propagate Knowledge in Web Ontologies. Workshop on  Uncertainty Reasoning for the Semantic Web (URSW 2014). [PDF]
  
• Volker Tresp and Maximilian Nickel. Relational Models. Draft of a paper in the  Encyclopedia of Social Network Analysis and Mining, Springer, 2014 (accepted 2012). [PDF]
• Volker Tresp, Yi Huang, and Maximilian Nickel. Querying the Web with Statistical Machine Learning. In Towards the Internet of Services: The THESEUS Program, 2014 (accepted 2012).  [PDF]
• Yi Huang, Volker Tresp, Maximilian Nickel, Achim Rettinger, and Hans-Peter Kriegel. A Scalable Approach for Statistical Learning in Semantic Graphs. Semantic Web – Interoperability, Usability, Applicability (SWJ), 2014 (accepted 2012). [PDF]
• Marco Balduini, Irene Celino, Daniele Dell’Aglio, Emanuele Della Valle, Yi Huang, Tony Lee, Seon-Ho Kim and Volker Tresp. Reality Mining on Micropost Streams: Deductive and Inductive Reasoning for Personalized and Location-based Recommendations. Semantic Web – Interoperability, Usability, Applicability (SWJ), 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.
  
• Maximilian Nickel and Volker Tresp. Tensor Factorization for Multi-Relational Learning. NECTAR track of the ECML/PKDD, 2013.  [PDF]
  
• Maximilian Nickel and Volker Tresp. An Analysis of Tensor Models for Learning on Structured Data. Proceedings of the ECML/PKDD, 2013. [PDF]
• Maximilian Nickel and Volker Tresp. Logistic Tensor Factorization for Multi-Relational Data. In Structured Learning: Inferring Graphs from Structured and Unstructured Inputs (SLG 2013). Workshop at the ICML, 2013. [PDF]
  
• Denis Krompaß, Maximilian Nickel, Xueyan Jiang, and Volker Tresp.  Non-Negative Tensor Factorization with RESCAL. ECML/PKDD 2013 Workshop on Tensor Methods for Machine Learning, 2013. [PDF]
• Xueyan Jiang, Volker Tresp and Denis Krompass. A Logistic Additive Model for Relation Prediction in Multi-relational data.  ECML/PKDD 2013 Workshop on Tensor Methods for Machine Learning, 2013. [PDF]
• Tony Kyung-il Lee, Seon-Ho Kim, Marco Balduini, Daniele Dell'Aglio, Irene Celino, Yi Huang, Volker Tresp and Emanuele Della Valle. Location-Based Mobile Recommendations by Hybrid Reasoning on Social Media Streams. JIST,  2013.  

• Xueyan Jiang, Volker Tresp, Yi Huang, and Maximilian Nickel. Link Prediction in Multi-relational Graphs using Additive Models. Proceedings of  International Workshop on Semantic Technologies meet Recommender Systems & Big Data at the ISWC, 2012. [PDF]
• Marco Balduini, Irene Celino, Daniele Dell’Aglio, Emanuele Della Vallea, Yi Huang, Tony Lee, Seon-Ho Kim, and Volker Tresp. BOTTARI: an Augmented Reality Mobile Application to deliver Personalized and Location-based Recommendations by Continuous Analysis of Social Media Streams. Journal of Web Semantics (JWS), 2012. [PDF]
  
• Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. Factorizing YAGO: Scalable Machine Learning for Linked Data. In Proceedings of the 21st International World Wide Web Conference (WWW2012), 2012. [PDF]
  
• Xueyan Jiang, Yi Huang,  Maximilian Nickel, and Volker Tresp. Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction. In Proceedings of the ESWC, 2012. [PDF]
• Xueyan Jiang, Volker Tresp, Yi Huang, Maximilian Nickel, and Hans-Peter Kriegel.  Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information. Proceedings of the ECML/PKDD, 2012. [PDF]
• Achim Rettinger, Uta Lösch, Volker Tresp, Claudia d’Amato, and Nicola Fanizzi. Mining the Semantic Web - Statistical learning for next generation knowledge bases. Data Mining and Knowledge Discovery (DATAMINE), Springer, 2012. [PDF]
• Achim Rettinger, Hendrik Wermser, Yi Huang, and Volker Tresp. Context-aware Tensor Decomposition for Relation Prediction in Social Networks. Social Network Analysis and Mining (SNAM), Springer, 2012. [PDF]
• Mohamed Yahya, Klaus Berberich, Shady Elbassuoni, Maya Ramanath, Volker Tresp, and Gerhard Weikum. Deep answers for naturally asked questions on the web of data. WWW (Companion Volume),   2012.  [PDF]
• 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}

• Emanuele Della Valle, Irene Celino,  Daniele Dell'Aglio, Ralph Grothmann, Florian Steinke, and Volker Tresp. Semantic Traffic-AwareRouting Using the LarKC Platform. IEEE Internet Computing, 2011. [PDF]
  
• Volker Tresp, Yi Huang, Xueyan Jiang, and Achim Rettinger. Graphical Models for Relations - Modeling Relational Context. International Conference on Knowledge Discovery and Information Retrieval, 2011. [PDF]
  
• Irene Celino, Daniele Dell'Aglio, Emanuele Della Valle, Yi Huang, Tony Lee, Seon-Ho Kim, and Volker Tresp.  Towards BOTTARI: Using Stream Reasoning to Make Sense of Location-Based Micro-Posts.  In: R. Garcia-Castro et al. (Eds.): ESWC 2011 Workshops, LNCS 7117, Springer, 2011. [PDF]
• Hendrik Wermser, Achim Rettinger, and Volker Tresp. Modeling and Learning Context-Aware Recommendation Scenarios using Tensor Decomposition. International Conference on Advances in Social Networks Analysis and Mining, 2011. [PDF]
• Joshua L. Moore, Florian Steinke, and Volker Tresp. A Novel Metric for Information Retrieval in Semantic Networks. ESWC 2011 Workshops, LNCS 7117, Springer, 2011. [PDF]
• Irene Celino, Daniele Dell'Aglio, Emanuele Della Valle, Ralph Grothmann, Florian Steinke, and Volker Tresp. Integrating Machine Learning in a Semantic Web Platform for Traffic Forecasting and Routing. In Proceedings of the 3rd International Workshop on Inductive Reasoning and Machine Learning for the Semantic Web (IRMLES),  2011. [PDF]
• Irene Celino, Daniele Dell'Aglio, Emanuele Della Valle, Yi Huang, Tony Lee, Stanley Park, and Volker Tresp. Making Sense of Location-based Micro-posts Using Stream Reasoning. In Proceedings of the Making Sense of Microposts Workshop (#MSM), 2011. [PDF]
• Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. A Three-Way Model for Collective Learning on Multi-Relational Data. In Proceedings of the 28th International Conference on Machine Learning, 2011. [PDF]

• Davide Barbieri, Daniele Braga, Stefano Ceri, Emanuele Della Valle, Yi Huang, Volker Tresp, Achim Rettinger, and Hendrik Wermser. Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics. IEEE Intelligent Systems, 99, 2010.  [PDF]
• Yi Huang, Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. A Scalable Kernel Approach  to Learning in Semantic Graphs with Applications to Linked Data. In Proc. of the 1st Workshop  on Mining the Future Internet, 2010.
  [PDF]
• Markus Bundschus, Anna Bauer-Mehren, Volker Tresp, Laura Furlong and Hans-Peter Kriegel. Digging for knowledge with information extraction: A case study on human gene-disease associations. In Proc. of the 19th ACM International Conference on Information and Knowledge Management (CIKM), 2010. [PDF]
• Achim Rettinger, Matthias Nickles, and Volker  Tresp. Statistical relational learning of trust. Machine Learning Journal, 81, 2010.[PDF]
  
• Yi Huang, Volker Tresp, Markus Bundschus, Achim Rettinger, and Hans-Peter Kriegel. Multivariate structured prediction for learning on the semantic web. In Proceedings of the 20th International Conference on Inductive Logic Programming (ILP), 2010. [PDF]
• Davide Magatti, Florian Steinke, Markus Bundschus, and Volker Tresp. Combined structured and keyword-based search in textually enriched entity-relationship graphs. First workshop on automated knowledge base construction (AKBC),  2010. [PDF]
• Maximilian Nickel and Volker Tresp, Three-Way DEDICOM for Relational Learning. NIPS Workshop:  Tensors, Kernels and Machine Learning, 2010
  

• Markus Bundschus, Volker Tresp, and Hans-Peter Kriegel. Topic models for semantically annotated document collections. In NIPS 2009 Workshop: Applications for Topic Models: Text and Beyond, 2009. [PDF]
• Yi Huang, Volker Tresp, and Hans-Peter Kriegel. Multivariate prediction for learning in relational graphs. In NIPS 2009 Workshop: Analyzing Networks and Learning With Graphs , 2009. [PDF]
• Markus Bundschus, Shipeng Yu, Volker Tresp, Achim Rettinger, Matthaeus Dejori, and Hans-Peter Kriegel. Hierarchical bayesian models for collaborative tagging systems. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2009. [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]
• Zhao Xu, Kristian Kersting, and Volker Tresp. Multi-relational learning with gaussian processes. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), July 2009.  [PDF]
• Zhao Xu, Volker Tresp, Achim Rettinger, and Kristian Kersting. Social network mining with nonparametric relational models. In H. Zhang, M. Smith, L. Giles, and J. Yen, editors, Advances in Social Network Mining and Analysis, LNCS. Springer, 2009.  [PDF]

• Markus Bundschus, Matthaeus Dejori, Martin Stetter, Volker Tresp, and Hans-Peter Kriegel. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics, 9:207, 2008. [PDF]
• Markus Bundschus, Matthaeus Dejori, Shipeng Yu, Volker Tresp, and Hans-Peter Kriegel. Statistical modeling of medical indexing processes for biomedical knowledge information discovery from text. In Proceedings of the 8th International Workshop on Data Mining in Bioinformatics (BIOKDD '08), 2008. [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]
• Christoph Lippert, Stefan-Hagen Weber, Yi Huang, Volker Tresp, Matthias Schubert, and Hans-Peter Kriegel. Relation-prediction in multi-relational domains using matrix-factorization. In NIPS 2008 Workshop: Structured Input - Structured Output, 2008. [PDF]
• Stefan Reckow and Volker Tresp. Integrating ontological prior knowledge into relational learning. In NIPS 2008 Workshop: Structured Input - Structured Output, 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]
• Zhao Xu, Volker Tresp, Shipeng Yu, and Kai Yu. Nonparametric relational learning for social network analysis. In 2nd ACM Workshop on Social Network Mining and Analysis (SNA-KDD 2008), 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]
• Anton Schwaighofer, Mathaeus Dejori, Volker Tresp, and Martin Stetter. Structure learning with nonparametric decomposable models. In Proceedings of ICANN 2007. Springer Verlag, 2007.  [PDF]
• Zhao Xu, Volker Tresp, Shipeng Yu, Kai Yu, and Hans-Peter Kriegel. Fast inference in infinite hidden relational models. In 5th International Workshop on Mining and Learning with Graphs (MLG 2007), 2007. [PDF]
• Shipeng Yu, Volker Tresp, and Kai Yu. Robust multi-task learning with t-processes. In 24th International Conference on Machine Learning (ICML'2007), 2007. [PDF]
• Yi Huang, Volker Tresp, and Stefan Hagen Weber. Predictive Modeling using Features derived from Paths in Relational Graphs. Technical report, 2007.  [PDF]
• Ruxandra Lupas Scheiterer, Dragan Obradovic, and Volker Tresp. Tailored-to-Fit Bayesian Network Modeling of Expert Diagnostic Knowledge.  The Journal of VLSI Signal Processing, Volume 49, Number 2, 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]
• Kai Yu, Jinbo Bi, and Volker Tresp. Active learning via transductive experimental design. In The 23nd International Conference on Machine Learning (ICML 2006), 2006. [PDF]
• Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, and Zhao Xu. Stochastic relational models for discriminative link prediction. In Advances in Neural Information Processing Systems (NIPS*2006). MIT Press, 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, and Hans-Peter Kriegel. Multi-output regularized feature projection. IEEE Transactions on Knowledge and Data Engineering, 18 (22), 2006. [PDF]
• Shipeng Yu, Kai Yu, Volker Tresp, and Hans-Peter Kriegel. Variational bayesian dirichlet-multinomial allocation for exponential family mixtures. In 17th European Conference on Machine Learning (ECML 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]

• 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 and Volker Tresp. Learning to learn and collaborative filtering. In Workshop on Inductive Transfer: 10 Years Later (NIPS*2005 Workshop), 2005.   [PDF]
• Kai Yu and Volker Tresp. Soft clustering on graphs. In Advances in Neural Information Processing Systems (NIPS*2005). MIT Press, 2005.   [PDF]
• Kai Yu, Volker Tresp, and Anton Schwaighofer. Learning gaussian processes from multiple tasks. 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. Dirichlet enhanced latent semantic analysis. In Workshop on Artificial Intelligence and Statistics AISTAT 2005, 2005.   [PDF]
• Kai Yu, Shipeng Yu, and Volker Tresp. Multi-label informed latent semantic indexing. In Proceedings of the 28th Annual International ACM SIGIR Conference, 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]
  
• Shipeng Yu, Kai Yu, Volker Tresp, and Hans-Peter Kriegel. A probabilistic clustering-projection model for discrete data. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 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]

• Mathäus Dejori, Anton Schwaighofer, Volker Tresp, and Martin Stetter. Mining functional modules in genetic networks with decomposable graphical models, 2004.  [PDF]
• Anton Schwaighofer, Volker Tresp, and Kai Yu. Learning gaussian process kernels via hierarchical bayes. In Advances in Neural Information Processing Systems (NIPS*2004). MIT Press, 2004.  [PDF]
• Kai Yu, Anton Schwaighofer, Volker Tresp, Xiaowei Xu, and Hans-Peter Kriegel. Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering (TKDE), 10, 2004.  [PDF]
• Kai Yu, Volker Tresp, and Shipeng Yu. A nonparametric hierarchical bayesian framework for information filtering. In Proceedings of the 27th Annual International ACM SIGIR Conference. ACM, 2004.  [PDF]
• Michael Haft, Reimar Hofmann, and Volker Tresp. Generative binary codes. Formal Pattern Analysis & Applications,  2004
• Kai Yu and Volker Tresp. Heterogenous Data Fusion via a Probabilistic Latent-Variable Model. Organic and Pervasive Computing – ARCS. Lecture Notes in Computer Science Volume 2981, 2004
  

• Kai Yu Kai, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, and HongJiang Zhang. Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical bayes. In Proceedings of 19th International Conference on Uncertainty in Artificial Intelligence (UAI'03)), 2003. [PDF]
• Anton Schwaighofer, Marian Grigoras, Volker Tresp, and Clemens Hoffmann. Gpps: A gaussian process positioning system for cellular networks. In Advances in Neural Information Processing Systems (NIPS*2003). MIT Press, 2003. [PDF]
• Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, and Jizhi Wang. Representative sampling for text classification using support vector machines. In 25th European Conference on Information Retrieval Research, ECIR'2003, 2003.  [PDF]
• Kai Yu, Wei-Ying Ma, Volker Tresp, Zhao Xu, Xiaofei He, HongJiang Zhang, and Hans-Peter Kriegel. Knowing a tree from the forest: Art image retrieval using a society of profiles. In Proc. of 11th Annual ACM International Conference on Multimedia (ACM Multimedia'03), 2003. [PDF]
• Volker Tresp and Kai Yu. An introduction to nonparametric hierarchical bayesian modelling with a focus on multi-agent learning. In Proceedings of the Hamilton Summer School on Switching and Learning in Feedback Systems. Lecture Notes in Computing Science, Volume 3355, 2003.  [PDF]
• Zhao Xu, Xiaowei Xu, Kai Yu, and Volker Tresp.  A Hybrid Relevance-Feedback Approach to Text Retrieval. In Advances in Information Retrieval. Lecture Notes in Computer Science Volume 2633, 2003. [PDF]
• Anton Schwaighofer, Volker Tresp, Peter Mayer, Andreas Krause, Jürgen Beuthan, Helmut Rost, Georg Metzger, Gerhard A. Müller, and Alexander K. Scheel. Classification of rheumatoid joint inflammation based on laser imaging. IEEE Trans. Biomed. Engineering 50, 2003.
  [PDF]

• Thomas Briegel and Volker Tresp. A nonlinear state space model for the blood glucose metabolism of a diabetic. at-Automatisierungstechnik, 50, 2002. [PDF]
• Alexander K. Scheel, Andreas Krause, Ingolf Mesecke von Rheinbaben, Georg Metzger, Helmut Rost, Volker Tresp, Peter Mayer, Monika Reuss-Borst, and Gerhard A. Müller. Assessment of Proximal Finger Joint Inflammation in Patients With Rheumatoid Arthritis, Using a Novel Laser-Based Imaging Technique. Arthritis and Rheumatism, 46(5), 2002. [PDF]
• Anton Schwaighofer and Volker Tresp. Transductive and inductive methods for approximate gaussian process regression. In Advances in Neural Information Processing Systems (NIPS*2002). MIT Press, 2002. [PDF]
• Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, and Gerhard A. Müller. The RA scanner: prediction of rheumatoid joint inflammation based on laser imaging. In Advances in Neural Information Processing Systems (NIPS*2002). MIT Press, 2002. [PDF]
• Volker Tresp. The equivalence between row and column linear regression. Technical report, 2002. [PDF]
• Christopher K. I. Williams, Carl Edward Rasmussen, Anton Schwaighofer, and Volker Tresp. Observations of the nyström method for gaussian process prediction. Technical report, University of Edinburgh, 2002.  [PDF]
• Kai Yu, Xiaowei Xu, Anton Schwaighofer, Volker Tresp, and Hans-Peter Kriegel. Removing redundancy and inconsistency in memory-based collaborative filtering.    Proceedings of the eleventh international conference on Information and knowledge management (CIKM '02), 2002. [PDF]

• Anton Schwaighofer and Volker Tresp. The Bayesian committee support vector machine. In International Conference on Artificial Neural Networks - ICANN 2001, 2001. [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, 2001. [PDF]
• Volker Tresp and Anton Schwaighofer. Local factorization of functions. Technical report, 2001. [PDF]
• Volker Tresp and Anton Schwaighofer. Scalable kernel systems. In International Conference on Artificial Neural Networks - ICANN 2001, 2001. [PDF]
• Joachim Horn, Thomas Birkhölzer, Oliver Hogl, Marco Pellegrino, Ruxandra Lupas Scheiterer, Kai-Uwe Schmidt, and Volker Tresp. Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System. In Artificial intelligence in medicine, LNCS, Springer, 2001.

• 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. A bayesian committee machine. Neural Computation, 12, 2000.  [PDF]
• Volker Tresp. The generalized bayesian committee machine. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2000. [PDF]
• Volker Tresp. Mixtures of gaussian processes. In Advances in Neural Information Processing Systems (NIPS*2000). MIT Press, 2000.  [PDF]

• Thomas Briegel and Volker Tresp. Robust neural network regression for offline and online learning. In Advances in Neural Information Processing Systems (NIPS*1999). MIT Press, 1999.  [PDF]
• Michael Haft, Reimar Hofmann, and Volker Tresp. Model-independent mean field theory as a local method for approximate propagation of information. Network: Computation in Neural Systems, 10, 1999.   [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]
• Volker Tresp, Thomas Briegel, and John Moody. Neural-network models for the blood glucose metabolism of a diabetic. IEEE Transactions on Neural Networks, 10, 1999.  [PDF]

• Thomas Briegel and Volker Tresp. Fisher scoring and a mixture of modes approach for approximate inference and learning in nonlinear state space models. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems (NIPS*1998). MIT Press, 1998. [PDF]
• Jaakko Hollmén and Volker Tresp. Call-based fraud detection in mobile communication networks using a hierarchical regime-switching model. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems (NIPS*1998). MIT Press, 1998.  [PDF]
• Dirk Ormoneit and Volker Tresp. Averaging, maximum penalized likelihood and bayesian estimation for improving gaussian mixture probability density estimates. IEEE Transactions on Neural Networks, 9, 1998. [PDF]
• Volker Tresp and Reimar Hofmann. Nonlinear time-series prediction with missing and noisy data. Neural Computation, 1998. [PDF]
• Michiaki Taniguchi, Michael Haft, Jaakko Hollmén, and Volker Tresp. Fraud detection in communication networks using neural and probabilsitic methods. IEEE International conference on acoustic speech and signal processing (ICASSP), 1998. [PDF]

• Thomas Briegel and Volker Tresp. A solution for missing data in recurrent neural networks with an application to blood glucose prediction. In M. I. Jordan, M. S. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems (NIPS*1997), 1997.  [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]
• Michiaki Taniguchi and Volker Tresp. Averaging regularized estimator. Neural Computation, 1997.  [PDF]
• Volker Tresp, Jürgen Hollatz, and Subutai Ahmad. Representing probabilistic rules with networks of gaussian basis functions. Machine Learning, 1997. [PDF]
• Michiaki Taniguchi and Volker Tresp. Combining Regularized Neural Networks. International Conference on Artificial Neural Networks (ICANN), 1997.

• 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]

• Reimar Hofmann and Volker Tresp. Discovering structure in continuous variables using bayesian networks. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems (NIPS*1995). MIT Press, 1995.  [PDF]
• Dirk Ormoneit and Volker Tresp. Improved gaussian mixture density estimates using bayesian penalty terms und network averaging. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems (NIPS*1995). MIT Press, 1995. [PDF]
• Volker Tresp and Reimar Hofmann. Missing and noisy data in nonlinear time-series prediction. In Neural Networks for Signal Processing 5. IEEE Signal Processing Society, 1995.  [PDF]
• Volker Tresp: Die besonderen Eigenschaften Neuronaler Netze bei der Approximation von Funktionen. Künstliche Intelligenz (KI), 1995.

• Volker Tresp and Michiaki Taniguchi. Combining estimators using non-constant weighting functions. In G. Tesauro, D. S. Touretzky, and Leen T. K., editors, Advances in Neural Information Processing Systems (NIPS*1994). MIT press, 1994.  [PDF]
• Volker Tresp, Ralph Neuneier, and Subutai Ahmad. Efficient methods for dealing with missing data in supervised learning. In G. Tesauro, D. S. Touretzky, and Leen T. K., editors, Advances in Neural Information Processing Systems (NIPS*1994). MIT Press, 1994.  [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]
• Subutai Ahmad and Volker Tresp. Classification with missing and uncertain inputs. IEEE International Conference on Neural Networks. 1993.
  

• Subutai Ahmad and Volker Tresp. Some solutions to the missing feature problem in vision. 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, 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.

• Martin Röscheisen, Reimar Hofmann, and Volker Tresp. Neural control for rolling mills: Incorporating domain theories to overcome data deficiency. [Geoff Hinton referred to the mixing in of prior data as: "Priors without Prejudice"] In J. E. Moody, Hanson S. J., and Lippmann R., editors, Advances in Neural Information Processing Systems (NIPS*1991),1991. [PDF]
• Volker Tresp. A Neural Architecture for 2D and 3D Vision. DAGM-Symposium, 1991. 

• Volker Tresp. Neural network approach for three-dimensional object recognition. In Richard Lippmann, John E. Moody, and David S. Touretzky, editors, Advances in Neural Information Processing Systems (NIPS*1990). Morgan Kaufmann, 1990.  [PDF]