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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  (8)
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); Artificial Intelligence (1);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (2)
Main Referenced Disciplines
Computer science (41)
Civil engineering (1)

Publications (total 42)

The most downloaded
1223 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

24 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., 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

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

XU, J., BIRYUKOV, M., THEOBALD, M., & Ellampallil Venugopal, V. (2023). BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph [Paper presentation]. EAI BDTA 2023 WAS HELD AS AN ON-SITE CONFERENCE.
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.

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.

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

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

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

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. (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., 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

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

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