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Publications

2024

  • Jan Hagnberger, Mario Kalimuthu, Daniel Musekamp, and Mathias Niepert. Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs. AI for Differential Equations in Science Workshop @ ICLR 2024.
  • Jan Hagnberger, Mario Kalimuthu, Daniel Musekamp, and Mathias Niepert. Vectorized Conditional Neural Fields for Computational Fluid Dynamics. Machine Learning for Fluid Dynamics Workshop organized by the European Research Community on Flow, Turbulence and Combustion.
  • Federico Errica and Mathias Niepert. Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. In Proceedings of the 12th International Conference on Learning Representations (ICLR 2024).
  • Anji Liu, Mathias Niepert, and Guy Van den Broeck. Image Inpainting via Tractable Steering of Diffusion Models. In Proceedings of the 12th International Conference on Learning Representations (ICLR 2024).
  • Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, and Christopher Morris. Probabilistically Rewired Message-Passing Neural Networks. In Proceedings of the 12th International Conference on Learning Representations (ICLR 2024).

2023

  • Duy Nguyen, Tan Pham, Nghiem Diep, Nghi Phan, Quang Pham, Vinh Tong, Binh Nguyen, Ngan Le, Nhat Ho, Pengtao Xie, Daniel Sonntag, and Mathias Niepert. On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation. Workshop on Robustness of Few-show and Zero-shot Learning in Large Foundation Models @ NeurIPS 2023.
  • Duy Nguyen, Hoang Nguyen, Nghiem Diep, Tan Pham, Tri Cao, Binh Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, and Mathias Niepert. LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
  • David Friede, Christian Reimers, Heiner Stuckenschmidt, and Mathias Niepert. Learning Distentangled Discrete Representations. In Proceedings of the 34th European Conference on Machine Learning (ECML 2023).
  • Makoto Takamoto, Francesco Alesiani, and Mathias Niepert. Learning Neural PDE Solvers with Parameter-Guided Channel Attention. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023).
  • Pasquale Minervini, Luca Franceschi, and Mathias Niepert. Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models. In Proceedings of the 37th Conference on Artificial Intelligence (AAAI 2023).
  • Kareem Ahmed, Zhe Zeng, Mathias Niepert, and Guy Van den Broeck. SIMPLE: A Gradient Estimator for k-Subset Sampling. In Proceedings of the 11th International Conference on Learning Representations (ICLR 2023).
  • Cheng Wang, Carolin Lawrence, and Mathias Niepert. State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions. Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 45, no. 06, 2023.
  • Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, and Christopher Morris. Probabilistic Task-Adaptive Graph Rewiring. Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators @ ICML 2023.
  • Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, and Hongyu Ren. Approximate Answering of Graph Queries. Compendium of Neurosymbolic Artificial Intelligence.

2022

  • Kareem Ahmed, Zhe Zeng, Mathias Niepert, and Guy Van den Broeck. SIMPLE: A Gradient Estimator for k-Subset Sampling. SoCal ML an NLP Symposium, 2022.
  • Makoto Takamoto, Francesco Alesiani, and Mathias Niepert. Channel-Attention-Based PDE Parameter Embeddings for SciML. Machine Learning and the Physical Sciences Workshop (ML4PS) @ NeurIPS.
  • Francesco Alesiani, Makoto Takamoto, and Mathias Niepert. HyperFNO: Improving the Generalization Behavior of Fourier Neural Operators. Machine Learning and the Physical Sciences Workshop (ML4PS) @ NeurIPS.
  • Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc Viet Hung Nguyen, Mathias Niepert. Joint Multilingual Knowledge Graph Completion and Alignment. In Findings of Empirical Natural Language Processing (Findings EMNLP 2022).
  • Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert. Ordered Subgraph Aggregation Networks. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
  • Wiem Ben Rim, Carolin Lawrence, Kiril Gashteovski, Mathias Niepert, Naoaki Okazaki. Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction. BlackboxNLP Workshop co-located with EMNLP 2022.
  • Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Dan MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert. PDEBench: An Extensive Benchmark for Scientific Machine Learning. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
  • Bhushan Kotnis, Kiril Gashteovski, Daniel Onoro Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence. Modular and Iterative Multilingual Open Information Extraction. (long paper) In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022).
  • Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš. BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation. (long paper) In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022).
  • Niklas Friedrich, Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert and Goran Glavaš. AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark. (demo paper) In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022).

2021

  • Mathias Niepert, Pasquale Minervini, and Luca Franceschi. Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions. In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
  • David Friede and Mathias Niepert. Efficient Learning of Discrete-Continuous Computation Graphs. In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
  • Roberto González, Claudio Soriente, Juan Miguel Carrascosa, Alberto Garcia-Duran, Costas Iordanou, Mathias Niepert. User Profiling by Network Observers. International Conference on emerging Networking EXperiments and Technologies (ACM CoNext 2021).
  • Wiem Ben Rim, Carolin Lawrence, Kiril Gashteovski, Mathias Niepert, Naoaki Okazaki. Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction. International Conference on Automated Knowledge Base Construction (AKBC 2021).
  • Alexander Jung, Hugo Lefeuvre, Charalampos Rotsos, Pierre Olivier, Daniel Oñoro-Rubio, Felipe Huici, Mathias Niepert. Wayfinder: towards automatically deriving optimal OS configurations 12th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2021).
  • Jun Cheng, Carolin Lawrence, Mathias Niepert. VEGN: Variant Effect Prediction with Graph Neural Networks. ICML Workshop on Computational Biology (WCB).
  • Cheng Wang, Carolin Lawrence, Mathias Niepert. Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs. In Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021). [arxiv]
  • Carolin Lawrence, Timo Sztyler, and Mathias Niepert. Explaining Neural Matrix Factorization with Gradient Rollback. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021).
  • Bhushan Kotnis, Carolin Lawrence, and Mathias Niepert. Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021),.
  • Brandon Malone, Alberto Garcia-Duran, and Mathias Niepert. Learning Representations of Missing Data using Graph Neural Networks for Predicting Patient Outcomes. AAAI'21 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI'21)
  • Zhao Xu, Daniel Onoro Rubio, Giuseppe Serra, and Mathias Niepert. Learning Sparsity of Representations with Discrete Latent Variables. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021).
  • Giuseppe Serra, Zhao Xu, Mathias Niepert, Carolin Lawrence, Peter Tiňo, and Xin Yao. Interpreting Node Embedding with Text-labeled Graphs. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021).

2020

  • Mathias Niepert, Guy Van den Broeck. Tractability through Exchangeability: The Statistics of Lifting. In An Introduction to Probabilistic Lifted Inference, MIT Press.
  • Mathias Niepert, Guy Van den Broeck. Lifted Markov Chain Monte Carlo. In An Introduction to Probabilistic Lifted Inference, MIT Press.
  • Carolin Lawrence, Timo Sztyler, and Mathias Niepert. Explaining Neural Matrix Factorization with Gradient Rollback. Women in Machine Learning Workshop @ NeurIPS.
  • Cheng Wang, Mathias Niepert, Hui Li. RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems IEEE Transactions on Neural Networks and Learning Systems.
  • Alberto Garcia-Duran, Roberto Gonzalez, Daniel Onoro-Rubio, Mathias Niepert, Hui Li. TransRev: Modeling Reviews as Translations from Users to Items. preprint [arxiv] In Proceedings of the European Conference on Information Retrieval (ECIR).
  • Cheng Wang, Carolin Lawrence, Mathias Niepert Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs preprint [arxiv]

2019

  • Carolin Lawrence, Bhushan Kotnis, and Mathias Niepert. Attending to Future Tokens For Bidirectional Sequence Generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Hong Kong.
  • Kosuke Akimoto, Takuya Hiraoka, Kunihiko Sadamasa, and Mathias Niepert. Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Hong Kong.
  • Cheng Wang, Mathias Niepert. State-Regularized Recurrent Neural Networks. In Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, USA.
  • Cheng Wang, Mathias Niepert. State-Regularized Recurrent Neural Networks. BlackboxNLP Workshop co-located with ACL 2019, Florence, Italy.
  • Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He. Learning Discrete Structures for Graph Neural Networks. In Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, USA.
  • Sebastijan Dumancic, Alberto Garcia-Duran, Mathias Niepert. A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China.
  • Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre. Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs. In Proceedings of the 1st Conference on Automated Knowledge Base Construction (AKBC), Amherst, Massachusetts.
  • Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He. Graph Structure Learning for GCNs. ICLR Workshop on Representation Learning on Graphs and Manifolds, New Orleans, USA.
  • Ye Liu, Hui Li, Alberto García-Durán, Mathias Niepert, Daniel Oñoro-Rubio, David S. Rosenblum. MMKG: Multi-Modal Knowledge Graphs. In Proceedings of the 16th Extended Semantic Web Conference (ESWC), Portoroz, Slovenia.

2018

  • Cheng Wang, Mathias Niepert, and Hui Li. LRMM: Learning to Recommend with Missing Modalities. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, Belgium.
  • Alberto Garcia-Duran, Sebastijan Dumancic, and Mathias Niepert. Learning Sequence Encoders for Temporal Knowledge Base Completion. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, Belgium.
  • Alberto Garcia-Duran and Mathias Niepert. KBLRN: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), Monterey, California. [arxiv][data]
  • Mathias Niepert and Alberto Garcia-Duran. Towards A Spectrum of Graph Convolutional Networks. In Proceedings of the 1st IEEE Data Science Workshop, Lausanne, Switzerland. [arxiv]
  • Daniel Oñoro-Rubio and Mathias Niepert. Contextual Hourglass Networks for Segmentation and Density Estimation. Proceedings of the 1st International Conference on Medical Imaging with Deep Learning (MIDL), Amsterdam, The Netherlands. [OpenReview] (winner of NVIDIA best poster award)
  • Daniel Oñoro-Rubio, Mathias Niepert, Roberto J. López-Sastre. Learning Short-Cut Connections for Object Counting. Proceedings of the 29th British Machine Vision Conference (BMVC), Newcastle upon Tyne, UK. [arxiv]
  • Sebastijan Dumancic, Alberto Garcia-Duran, and Mathias Niepert. On Embeddings as an Alternative Paradigm for Relational Learning. Proceedings of the 8th International Workshop on Statistical Relational AI (StaRAI), Stockholm, Sweden, 2018. [arxiv]
  • Florian Schmidt, Mathias Niepert, and Felipe Huici. Representation Learning for Resource Usage Prediction. Proceedings of the 1st SysML Conference, Stanford, USA. [arxiv]
  • Nicolas Weber, Florian Schmidt, Mathias Niepert, and Felipe Huici. BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism. preprint [arxiv]
  • Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre. Representation Learning for Visual-Relational Knowledge Graphs. KDD Deep Learning Day, London, UK. preprint [arxiv][data]

2017

  • Alberto Garcia-Duran and Mathias Niepert. Learning Graph Representations with Embedding Propagation. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, USA, 2017.
  • Roberto Gonzalez, Filipe Manco, Alberto Garcia-Duran, Jose Mendes, Felipe Huici, Saverio Niccolini, and Mathias Niepert. Net2vec: Deep Learning for the Network. Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big-DAMA, SIGCOMM 2017
  • Roberto Gonzalez, Alberto García-Durán, Filipe Manco, Mathias Niepert, and Pelayo Vallina. Network Data Monetization Using Net2Vec. Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM) 2017.
  • Jakob Huber, Mathias Niepert, Jan Noessner, Joerg Schoenfisch, Christian Meilicke, and Heiner Stuckenschmidt. An Infrastructure for Probabilistic Reasoning with Web Ontologies. Semantic Web Journal, 2017.
  • Konstantin Kutzkov, Mathias Niepert, and Mohamed Ahmed. Scalable Regression Tree Learning in Data Streams. [paper]

2016

  • Mathias Niepert. Discriminative Gaifman Models. In Proceedings of the 30th Annual Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016.
  • Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. Learning Convolutional Neural Networks for Graphs. In Proceedings of the 33rd International Conference on Machine Learning (ICML), New York City, 2016. [slides]

2015

  • Mathias Niepert and Pedro Domingos. Learning and Inference in Tractable Probabilistic Knowledge Bases. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence. (UAI), Amsterdam, The Netherlands, 2015
  • Guy Van den Broeck and Mathias Niepert. Lifted Probabilistic Inference for Asymmetric Graphical Models. Proceedings of the 29th Conference on Artificial Intelligence. (oral presentation) (AAAI), Austin, Texas, 2015.

2014

  • Mathias Niepert, Pedro Domingos, and Jeff Bilmes. Generalized Conditional Independence and Decomposition Cognizant Curvature: Implications for Function Optimization. NIPS Workshop on Discrete and Combinatorial Problems in Machine Learning (DISCML) 2014. [paper]
  • Mathias Niepert and Sameer Singh. Out of Many, One: Unifying Web-Extracted Knowledge Bases. NIPS Workshop on Automated Knowledge Base Construction (AKBC) 2014.
  • Mathias Niepert and Pedro Domingos. Exchangeable Variable Models. In Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, 2014. (also accepted for presentation at the ICML Learning Tractable Probabilsitic Models Workshop) [paper][slides][code and data] [video]
  • Mathias Niepert and Guy Van den Broeck. Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference. In Proceedings of the 28th Conference on Artificial Intelligence (AAAI), Quebec City, Canada, 2014. (one of 5 papers nominated for the AAAI outstanding paper award); also accepted for presentation at the SIGMOD/PODS Big Uncertain Data Workshop) [paper]
  • Mathias Niepert and Pedro Domingos. Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond. In Proceedings of the 4th Workshop on Statistical Relational AI (StaRAI), Quebec City, Canada, 2014. [paper]
  • Jakob Huber, Timo Sztyler, Jan Noessner, Jaimie Murdock, Colin Allen, and Mathias Niepert. LODE: Linking Digital Humanities Content to the Web of Data. In Proceedings of the 14th ACM/IEEE Joint Conference on Digital Libraries (JCDL), London, UK, 2014. [paper]
  • Marc Gyssens, Mathias Niepert, and Dirk Van Gucht. On the completeness of the semigraphoid axioms for deriving arbitrary from saturated conditional independence statements. Information Processing Letters, 2014. [article]
  • Jan Noessner, Heiner Stuckenschmidt, Christian Meilicke, and Mathias Niepert. Completeness and Optimality in Ontology Alignment Debugging. Proceedings of the 9th International Workshop on Ontology Matching, Trento, Italy, 2014. [paper]

2013

  • Mathias Niepert. Symmetry-Aware Marginal Density Estimation. In Proceedings of the 27th Conference on Artificial Intelligence (AAAI), Bellevue, Washington, USA, 2013. (also accepted for presentation at the Statistical Relational AI workshop) [paper][code]
  • Mathias Niepert, Bassem Sayrafi, Marc Gyssens, and Dirk Van Gucht. The Conditional Independence Implication Problem: A Lattice-Theoretic Approach. Artificial Intelligence 202:29-51, 2013. [article]
  • Mathias Niepert. Statistical Relational Data Integration for Information Extraction. Reasoning Web, Springer, Heidelberg, 2013.
  • Daniel Fleischhacker, Christian Meilicke, Johanna Voelker, and Mathias Niepert. Computing Incoherence Explanations for Learned Ontologies. In Proceedings of the 7th International Conference on Web Reasoning and Rule Systems, Mannheim, Germany, 2013.
  • Arnab Dutta, Mathias Niepert, Christian Meilicke, and Simone Ponzetto. Integrating Open and Closed Information Extraction - Challanges and First Steps. In Proceedings of the NLP & DBpedia workshop co-located with ISWC, Sydney, Australia, 2013.
  • Jan Noessner, Mathias Niepert, and Heiner Stuckenschmidt. RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models. In Proceedings of the 27th Conference on Artificial Intelligence (AAAI), Bellevue, Washington, USA, 2013. (also accepted for presentation at the Statistical Relational AI workshop) [paper][code]

2012

  • Mathias Niepert. Lifted Probabilistic Inference: An MCMC Perspective. In Proceedings of the 2nd International Workshop on Statistical Relational AI (StaR AI), Catalina Island, USA, 2012. [paper][code]
  • Mathias Niepert. Markov Chains on Orbits of Permutation Groups. In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, USA, 2012. [paper][code]
  • Mathias Niepert, Christian Meilicke, and Heiner Stuckenschmidt. Towards Distributed MCMC Inference in Probabilistic Knowledge Bases. NAACL-HLT Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX), Montreal, Canada, 2012. [paper]
  • Henrik Leopold, Mathias Niepert, Matthias Weidlich, Jan Mendling, Remco Dijkman, and Heiner Stuckenschmidt. Probabilistic Optimization of Semantic Process Model Matching. In Proceedings of the 10th International Conference on Business Process Management (BPM), Tallinn, Estonia, 2012.
  • Rim Helaoui, Daniele Riboni, Mathias Niepert, Claudio Bettini and Heiner Stuckenschmidt. Towards Activity Recognition Using Probabilistic Description Logics. In Proceedings of the AAAI workshops, Toronto, Canada, 2012.

2011

  • Mathias Niepert. Reasoning under Uncertainty with Log-Linear Description Logics. In Proceedings of the 7th International Workshop on Uncertain Reasoning for the Semantic Web (URSW) at ISWC, Bonn, Germany, 2011. [paper][code]
  • Caecilia Zirn, Mathias Niepert, Heiner Stuckenschmidt, and Michael Strube. Fine-Grained Sentiment Analysis with Structural Features. In Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP), Chiang Mai, Thailand, 2011. (best paper award) [paper]
  • Mathias Niepert, Jan Noessner, and Heiner Stuckenschmidt. Log-Linear Description Logics. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011. (selected for oral presentation) [paper][code]
  • Jan Noessner, Mathias Niepert and Heiner Stuckenschmidt. Coherent Top-k Ontology Alignment for OWL EL. In Proceedings of the the 5th International Conference on Scalable Uncertainty Management (SUM), Dayton, Ohio, Springer-Verlag, 2011. [paper]
  • Rim Helaoui, Mathias Niepert and Heiner Stuckenschmidt. Recognizing Interleaved and Concurrent Activities Using Qualitative and Quantitative Temporal Relationships. Pervasive and Mobile Computing, Volume 7, Issue 6, Elsevier, 2011. [paper]
  • Jan Noessner and Mathias Niepert. ELOG: A Probabilistic Reasoner for OWL EL. In Proceedings of the the 5th International Conference on Web Reasoning and Rule Systems (RR), Galway, Ireland, Springer-Verlag, 2011. [paper]
  • Mathias Niepert, Jan Noessner, Christian Meilicke, and Heiner Stuckenschmidt. Probabilistic-Logical Web Data Integration. In Reasoning Web: 7th International Summer School 2011, Galway, Ireland, Springer-Verlag, 2011. [paper]
  • Johanna Voelker and Mathias Niepert. Statistical Schema Induction. In Proceedings of the 8th Extended Semantic Web Conference (ESWC), Heraklion, Greece, Springer-Verlag, 2011. [paper]
  • Rim Helaoui, Mathias Niepert, and Heiner Stuckenschmidt. Recognizing Interleaved and Concurrent Activities: A Statistical Relational Approach. In Proceedings of the 9th IEEE International Conference on Pervasive Computing and Communications (PerCom), Seattle, Washington, 2011. [paper]

2010

  • Mathias Niepert. A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, California, AUAI Press, 2010. [paper]
  • Mathias Niepert, Christian Meilicke, and Heiner Stuckenschmidt. A Probabilistic-Logical Framework for Ontology Matching. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI), Atlanta, Georgia, AAAI Press, 2010. (selected as exceptional top 4% paper) [paper][poster]
  • Jan Noessner and Mathias Niepert. CODI: Combinatorial Optimization for Data Integration. In Proceedings of the 5th International Workshop on Ontology Matching (OM), Shanghai, China, 2010. [paper]
  • Kai Eckert, Mathias Niepert, Christof Niemann, Cameron Buckner, Colin Allen, and Heiner Stuckenschmidt. Crowdsourcing the Assembly of Concept Hierarchies. In Proceedings of the 10th ACM/IEEE Joint Conference on Digital Libraries (JCDL), Gold Coast, Australia, ACM Press, 2010. [paper]
  • Nikolas Schmitt, Mathias Niepert, and Heiner Stuckenschmidt. BRAMBLE: A Web-based Framework for Interactive RDF-Graph Visualisation. International Semantic Web Conference (ISWC) 2010. (demo paper)
  • Mathias Niepert. Towards Collaboratively Learning and Populating Ontologies for the Social-Semantic Web. SIGWEB Newsletter, Spring 2010, ACM Press.
  • Jan Noessner, Mathias Niepert, Christian Meilicke, and Heiner Stuckenschmidt. Leveraging Terminological Structure for Object Reconciliation. In Proceedings of the 7th Extended Semantic Web Conference (ESWC), Heraklion, Greece, Springer-Verlag, 2010. (best paper award) [paper]
  • Mathias Niepert, Dirk Van Gucht, and Marc Gyssens. Logical and Algorithmic Properties of Stable Conditional Independence. International Journal of Approximate Reasoning, Volume 51, Issue 5, pages 531-543, 2010. [preprint]
  • Cameron Buckner, Mathias Niepert, and Colin Allen. From Encyclopedia to Ontology: Toward A Dynamic Representation of the Discipline of Philosophy. Synthese, Springer-Verlag, 2010. [article]
  • Robert Meusel, Mathias Niepert, Kai Eckert, and Heiner Stuckenschmidt. Thesaurus Extension Using Web Search Engines. In Proceedings of the 12th International Conference on Asia-Pacific Digital Libraries (ICADL), Gold Coast, Australia, Springer-Verlag, 2010. [paper]

2009 and earlier

  • Mathias Niepert. Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI), Montreal, Canada, pages 428-435, AUAI Press, 2009. [paper]
  • Mathias Niepert, Cameron Buckner, and Colin Allen. Working the Crowd: Design Principles and Early Lessons from the Social-Semantic Web. In Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web - (SW)^2  at ACM Hypertext, Turin, Italy, 2009. [paper]
  • Mathias Niepert, Dirk Van Gucht, and Marc Gyssens. On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI), Helsinki, Finland, pages 435-443, AUAI Press, 2008. (best student paper runner-up award) [paper]
  • Colin Allen, Cameron Buckner, and Mathias Niepert. The World is Not Flat: Expertise and InPhO. Selected papers from the Ninth Annual WebWise Conference. First Monday, Volume 13, Number 8, 2008. [article]
  • Mathias Niepert, Cameron Buckner, and Colin Allen. A Dynamic Ontology for a Dynamic Reference Work. In Proceedings of the 7th ACM/IEEE Joint Conference on Digital Libraries (JCDL), Vancouver, British Columbia, pages 288-297, ACM Press, 2007. [paper]