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Arnab Sharma, M.Tech

Address: Arnab Sharma
Paderborn University
Faculty of Electrical Engineering, Computer Science and Mathematics
Warburger Str. 100
D-33098 Paderborn, Germany
Office:

O4.131
Phone: +49 (0) 5251-60-5388
Fax:  +49 (0) 5251-60-3993 

Email:

 arnab.sharma(at)uni-paderborn.de
Secretary: Elisabeth Schlatt
Phone: +49 (0) 5251-60-3764
Email: schlatt(at)mail.upb.de
Office: O4.125

Pub­lic­a­tions

Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets

S.B. Schneider, A. Sharma, H. Karl, H. Wehrheim, in: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP, Washington, DC, USA, 2019, pp. 116--124.

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Testing Balancedness of ML Algorithms

A. Sharma, H. Wehrheim, in: Proceedings of the Software Engineering Conference (SE), Gesellschaft für Informatik e.V. (GI), Stuttgart, 2019, pp. 157–158.


Testing Machine Learning Algorithms for Balanced Data Usage

A. Sharma, H. Wehrheim, in: IEEE International Conference on Software Testing, Verification and Validation (ICST), IEEE, Xi’an, China, 2019, pp. 125--135.


Testing Balancedness of ML Algorithms

A. Sharma, H. Wehrheim, in: S. Becker, I. Bogicevic, G. Herzwurm, S. Wagner (Eds.), Software Engineering and Software Management, {SE/SWM} 2019, Stuttgart, Germany, February 18-22, 2019, {GI}, 2019, pp. 157–158.

DOI

Automatic Fairness Testing of Machine Learning Models

A. Sharma, H. Wehrheim, in: Proceedings of the 32th IFIP International Conference on Testing Software and Systems (ICTSS), Springer, n.d.


Testing Monotonicity of Machine Learning Models

A. Sharma, H. Wehrheim, CoRR abs/2002.12278 (2020).


Higher Income, Larger Loan? Monotonicity Testing of Machine Learning Models

A. Sharma, H. Wehrheim, in: Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)., ACM, n.d.


MLCheck- Property-Driven Testing of Machine Learning Models

A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, CoRR abs/2105.00741 (2021).


MLCHECK–Property-Driven Testing of Machine Learning Classifiers

A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, in: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, n.d.


Property-Driven Testing of Black-Box Functions

A. Sharma, V. Melnikov, E. Hüllermeier, H. Wehrheim, in: Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE, 2022, pp. 113–123.


Composition Analysis in Unknown Contexts

H. Wehrheim, E. Hüllermeier, S. Becker, M. Becker, C. Richter, A. Sharma, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 105–123.

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Testing of machine learning algorithms and models

A. Sharma, Testing of Machine Learning Algorithms and Models, 2023.

DOI

Trading-Off Interpretability and Accuracy in Medical Applications: A Study Toward Optimal Explainability of Hoeffding Trees

A. Sharma, D. Leite, C. Demir, A.-C.N. Ngomo, in: 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2024.

DOI

Interpretability Index Based on Balanced Volumes for Transparent Models and Agnostic Explainers

D. Leite, A. Sharma, C. Demir, A.-C. Ngomo, in: 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2024.

DOI

Inference over Unseen Entities, Relations and Literals on Knowledge Graphs

C. Demir, N.J. KOUAGOU, A. Sharma, A.-C. Ngonga Ngomo, Arxiv (2024).

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EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams

D. Leite, A. Silva, G. Casalino, A. Sharma, D. Fortunato, A.-C. Ngomo, in: 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), IEEE, 2024.

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Resilience in Knowledge Graph Embeddings

A. Sharma, N.J. KOUAGOU, A.-C. Ngonga Ngomo, Arxiv (2024).

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Adaptive Stochastic Weight Averaging

C. Demir, A. Sharma, A.-C. Ngonga Ngomo, Arxiv (2024).

DOI

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Further information:
Research interest

Testing of Machine Learning programs.