Volker
Tresp
Professor
Ludwig
Maximilian University of Munich
Distinguished
Research Scientist
Siemens
E-mail (email): volker.tresp at lmu dot de
Prospective students: As LMU
professor, I can supervise Ph.D. students. We are
looking for students who are looking for master thesis projects
and who have a strong mathematical background!
Current Research Interests:
News | Research Interests
| Biography
| Students | Past Students | Awards and Honors | Tutorials | Software | Papers
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 Schoo l
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 Research Talks in 2016 at ETH, MIT, Yale, UMass, Oxford
and Cambridge
Research
Interests
My current research interests focus on Statistical Relational
Learning, which combines machine learning with relational data
models and first-order logic and enables machine learning in
knowledge bases. An aspect of particular interest is that machine
learning tasks such as classification and object recognition can be
supported by rich background knowledge.
More 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
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
Software
Books
Papers
2023
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.
2022
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]
2021
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 Fact s.
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 ]
2020
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 Dynamic s, 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 ]
2019
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
2018
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]
2017
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.
2016
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 985 3, 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)
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]
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
2014
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]
2013
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.
2012
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 .
WW W (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}
2011
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]
2010
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 t he
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
2009
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]
2008
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 ]
2007
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 ]
2006
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 ]
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 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 ]
2004
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
2003
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 ]
2002
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 . P roceedings
of the eleventh international conference on
Information and knowledge management (CIKM '02 ),
2002. [PDF ]
2001
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.
2000
1999
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 ]
1998
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 ]
1997
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.
1996
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 ]
1995
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.
1994
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 ]
1993
1992
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.
1991
1990