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THEOBALD Martin

University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

Main Referenced Co-authors
DALLE LUCCA TOSI, Mauro  (9)
ELLAMPALLIL VENUGOPAL, Vinu  (8)
Gurajada, Sairam (4)
Papaioannou, Katerina (4)
Böhlen, Michael H. (3)
Main Referenced Keywords
Big Data (5); Stream data processing (4); sustainable-throughput (4); Neural Networks (3); concept drift (2);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (2)
Main Referenced Disciplines
Computer science (42)
Civil engineering (1)

Publications (total 43)

The most downloaded
1309 downloads
Nguyen, D. B., Abujabal, A., Tran, K., THEOBALD, M., & Weikum, G. (2017). Query-Driven On-The-Fly Knowledge Base Construction. Proceedings of the VLDB Endowment, 11 (1), 66--79. doi:10.14778/3136610.3136616 https://hdl.handle.net/10993/34035

The most cited

26 citations (Scopus®)

Nguyen, D. B., Abujabal, A., Tran, K., THEOBALD, M., & Weikum, G. (2017). Query-Driven On-The-Fly Knowledge Base Construction. Proceedings of the VLDB Endowment, 11 (1), 66--79. doi:10.14778/3136610.3136616 https://hdl.handle.net/10993/34035

DALLE LUCCA TOSI, M., & THEOBALD, M. (2024). OPTWIN: Drift Identification with Optimal Sub-Windows. 2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW). doi:10.1109/icdew61823.2024.00049
Peer reviewed

DALLE LUCCA TOSI, M., Venugopal, V. E., & THEOBALD, M. (2024). TensAIR: Real-Time Training of Neural Networks from Data-streams. In ICMLSC '24: Proceedings of the 2024 8th International Conference on Machine Learning and Soft Computing (pp. 73-82). Association for Computing Machinery. doi:10.1145/3647750.3647762
Peer reviewed

XU, J., BIRYUKOV, M., THEOBALD, M., & Ellampallil Venugopal, V. (2024). BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph. In Big Data Technologies and Applications. Springer Cham. doi:10.1007/978-3-031-52265-9_3
Peer reviewed

DALLE LUCCA TOSI, M., & THEOBALD, M. (2023). Convergence Analysis of Decentralized ASGD. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/56001.

DALLE LUCCA TOSI, M., & THEOBALD, M. (2023). OPTWIN: Drift identification with optimal sub-windows. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/55440.

TEMPERONI, A., DALLE LUCCA TOSI, M., & THEOBALD, M. (2023). Efficient Hessian-based DNN Optimization via Chain-Rule Approximation. In Proceedings of the 6th Joint International Conference on Data Science Management of Data (10th ACM IKDD CODS and 28th COMAD) (pp. 297--298).
Peer reviewed

Venugopal, V. E., THEOBALD, M., Tassetti, D., Chaychi, S., & Tawakuli, A. (25 July 2022). Targeting a light-weight and multi-channel approach for distributed stream processing. Journal of Parallel and Distributed Computing, 167, 77-96. doi:10.1016/j.jpdc.2022.04.022
Peer reviewed

Skorski, M., TEMPERONI, A., & THEOBALD, M. (2022). Robust and Provable Guarantees for Sparse Random Embeddings. In Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part II (pp. 211-223). Springer. doi:10.1007/978-3-031-05936-0\_17
Peer reviewed

DALLE LUCCA TOSI, M., ELLAMPALLIL VENUGOPAL, V., & THEOBALD, M. (2022). CONVERGENCE TIME ANALYSIS OF ASYNCHRONOUS DISTRIBUTED ARTIFICIAL NEURAL NETWORKS [Poster presentation]. 5th Joint International Conference on Data Science Management of Data (9th ACM IKDD CODS and 27th COMAD).
Peer reviewed

DALLE LUCCA TOSI, M., Ellampallil Venugopal, V., & THEOBALD, M. (2022). Convergence time analysis of Asynchronous Distributed Artificial Neural Networks. In 5th Joint International Conference on Data Science Management of Data (9th ACM IKDD CODS and 27th COMAD) (pp. 314--315).
Peer reviewed

DALLE LUCCA TOSI, M., ELLAMPALLIL VENUGOPAL, V., & THEOBALD, M. (2022). TensAIR: Real-Time Training of Neural Networks from Data-streams. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/54534.

Skorski, M., TEMPERONI, A., & THEOBALD, M. (2021). Revisiting Weight Initialization of Deep Neural Networks. In Proceedings of Machine Learning Research (pp. 1192-1207). PMLR.
Peer reviewed

DALLE LUCCA TOSI, M., THEOBALD, M., & ELLAMPALLIL VENUGOPAL, V. (21 May 2021). Online Learning using Distributed Neural Networks [Poster presentation]. DTU DRIVEN Colloquium, Luxembourg.

ELLAMPALLIL VENUGOPAL, V., & THEOBALD, M. (11 December 2020). Asynchronous Stream Data Processing using a Light-Weight and High-Performance Dataflow Engine [Paper presentation]. The Dutch-Belgian DataBase Day (DBDBD) 2020, Brussels, Belgium.

ELLAMPALLIL VENUGOPAL, V., & THEOBALD, M. (10 December 2020). Effective Stream Data Processing using Asynchronous Iterative Routing Protocol [Poster presentation]. IEEE International Conference on Big Data (IEEE BigData 2020).

Munoz-Velasco, E., Ozaki, A., & THEOBALD, M. (Eds.). (2020). 27th International Symposium on Temporal Representation and Reasoning, TIME 2020, September 23-25, 2020, Bozen-Bolzano, Italy. Wadern, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.

ELLAMPALLIL VENUGOPAL, V., THEOBALD, M., CHAYCHI, S., & TAWAKULI, A. (2020). AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing. In AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing (pp. 51-58). IEEE.
Peer reviewed

Wu, Y., Chen, J., Haxhidauti, P., ELLAMPALLIL VENUGOPAL, V., & THEOBALD, M. (2020). Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback. In Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback (pp. 92–106).
Peer reviewed

ELLAMPALLIL VENUGOPAL, V., & THEOBALD, M. (2020). Benchmarking Synchronous and Asynchronous Stream Processing Systems. In V. ELLAMPALLIL VENUGOPAL & M. THEOBALD, Benchmarking Synchronous and Asynchronous Stream Processing Systems (pp. 322-323). Esch-sur-Alzette, Luxembourg: ACM. doi:10.1145/3371158.3371206
Peer reviewed

Ben Amor, N., Quost, B., & THEOBALD, M. (Eds.). (2019). Scalable Uncertainty Management - 13th International Conference (SUM 2019), Compiegne, France, December 16-18, 2019, Proceedings. Springer.

Papaioannou, K., THEOBALD, M., & Böhlen, M. H. (2019). Outer and Anti Joins in Temporal-Probabilistic Databases. In 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8-11, 2019. IEEE. doi:10.1109/ICDE.2019.00187
Peer reviewed

Papaioannou, K., THEOBALD, M., & Böhlen, M. H. (04 October 2019). Lineage-Aware Temporal Windows: Supporting Set Operations in Temporal-Probabilistic Databases. CoRR, abs/1910.00474. doi:10.1109/ICDE.2018.00109

Van den Heuvel, M., Ivanov, P., Gatterbauer, W., Geerts, F., & THEOBALD, M. (2019). Anytime Approximation in Probabilistic Databases via Scaled Dissociations. In Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, Amsterdam, The Netherlands, June 30 - July 5, 2019. ACM. doi:10.1145/3299869.3319900
Peer reviewed

Papaioannou, K., THEOBALD, M., & Böhlen, M. H. (21 May 2019). Generalized Lineage-Aware Temporal Windows: Supporting Outer and Anti Joins in Temporal-Probabilistic Databases. CoRR, abs/1902.04379.
Peer reviewed

THEOBALD, M. (14 May 2019). From Big Data to Big Knowledge - Large-Scale Information Extraction Based on Statistical Methods (Invited Talk) [Paper presentation]. {SOFSEM} 2019: Theory and Practice of Computer Science - 45th International Conference on Current Trends in Theory and Practice of Computer Science, Novy Smokovec, Slovakia, January 27-30, 2019, Proceedings.

Fletcher, & THEOBALD, M. (2019). Indexing for Graph Query Evaluation. In S. Sakr & A. Y. Zomaya, Encyclopedia of Big Data Technologies. Springer.

d'Amato, C., & THEOBALD, M. (Eds.). (2018). Reasoning Web. Learning, Uncertainty, Streaming, and Scalability - 14th International Summer School 2018, Esch-sur-Alzette, Luxembourg, September 22-26, 2018, Tutorial Lectures. Heidelberg, Germany: Springer.

Saberi, M., THEOBALD, M., Hussain, O. K., Chang, E., & Hussain, F. K. (13 September 2018). Interactive feature selection for efficient customer recognition in contact centers: Dealing with common names. Expert Systems with Applications, 113, 356--376. doi:10.1016/j.eswa.2018.07.012
Peer Reviewed verified by ORBi

Van den Heuvel, M., Geerts, F., THEOBALD, M., & Getterbauer, W. (08 August 2018). A General Framework for Anytime Approximation in Probabilistic Databases. CoRR, abs/1806.10078.
Peer reviewed

Papaioannou, K., THEOBALD, M., & Böhlen, M. (2018). Supporting Set Operations in Temporal-Probabilistic Databases. In Proceedings of the 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16-19, 2018 (pp. 1180--1191). IEEE Computer Society.
Peer reviewed

BOUVRY, P., Bisdorff, R., SCHOMMER, C., SORGER, U., THEOBALD, M., & VAN DER TORRE, L. (2018). Proceedings - 2017 ILILAS Distinguished Lectures. Luxembourg, Luxembourg: University of Luxembourg. https://orbilu.uni.lu/handle/10993/33848

BENZMÜLLER, C., Lisetti, C., & THEOBALD, M. (Eds.). (2017). GCAI 2017: 3rd Global Conference on Artificial Intelligence, Miami, FL, USA, 18-22 October 2017. EPiC Series in Computing, EasyChair.

Nanda, R., Siragusa, G., Di caro, L., THEOBALD, M., Boella, G., ROBALDO, L., & Costamagna, F. (2017). Concept Recognition in European and National Law. In proc. of The 30th international conference on Legal Knowledge and Information Systems (JURIX 2017).
Peer reviewed

Nguyen, D. B., Abujabal, A., Tran, K., THEOBALD, M., & Weikum, G. (2017). Query-Driven On-The-Fly Knowledge Base Construction. Proceedings of the VLDB Endowment, 11 (1), 66--79. doi:10.14778/3136610.3136616
Peer Reviewed verified by ORBi

Nguyen, D. B., THEOBALD, M., & Weikum, G. (2017). J-REED: Joint Relation Extraction and Entity Disambiguation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06 - 10, 2017 (pp. 2227-2230).
Peer reviewed

Dylla, M., & THEOBALD, M. (2016). Learning Tuple Probabilities. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/34037.

Wang, Y., Ren, Z., THEOBALD, M., Dylla, & Melo, G. D. (2016). Summary Generation for Temporal Extractions. In Database and Expert Systems Applications - 27th International Conference (DEXA 2016). Springer. doi:10.1007/978-3-319-44403-1_23
Peer reviewed

Gurajada, S., & THEOBALD, M. (2016). Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/34036.

Gurajada, S., & THEOBALD, M. (2016). Distributed Set Reachability. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26 - July 01, 2016 (pp. 1247-1261).
Peer reviewed

Nguyen, D. B., THEOBALD, M., & Weikum, G. (2016). J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features. TACL, 4, 215--229.
Peer reviewed

Dylla, M., THEOBALD, M., & Miliaraki, I. (2014). Querying and Learning in Probabilistic Databases. In Reasoning Web. Reasoning on the Web in the Big Data Era - 10th International Summer School 2014, Athens, Greece, September 8-13, 2014. Proceedings. Springer.
Peer reviewed

Gurajada, S., Seufert, S., Miliaraki, I., & THEOBALD, M. (2014). TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing. In International Conference on Management of Data (SIGMOD 2014) (pp. 289-300). ACM.
Peer reviewed

Gurajada, S., Seufert, S., Miliaraki, I., & THEOBALD, M. (2014). Using Graph Summarization for Join-Ahead Pruning in a Distributed RDF Engine. In Semantic Web Information Management on Semantic Web Information Management (pp. 1-4). New York, NY, USA, Unknown/unspecified: ACM.
Peer reviewed

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