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Singh Akash

HEC Liège Research: Business Analytics & Supply Chain Mgmt

See author's contact details
ORCID
0000-0003-1679-8232
Main Referenced Co-authors
Ittoo, Ashwin  (5)
Ars, Pierre  (4)
Vandomme, Elise  (4)
Hellinckx, Peter (2)
Latré, Steven (2)
Main Referenced Keywords
Uncertainty (6); Machine learning (4); Churn (2); Conformal prediction (2); Deep reinforcement learning (2);
Main Referenced Unit & Research Centers
Digital Labs (1)
Digital lab (1)
IDLab, university of Antwerpen (1)
Main Referenced Disciplines
Computer science (4)
Quantitative methods in economics & management (2)
Engineering, computing & technology: Multidisciplinary, general & others (2)
Business & economic sciences: Multidisciplinary, general & others (1)
Google Scholar

Publications (total 9)

The most downloaded
292 downloads
Singh, A., Ittoo, A., Vandomme, E., & Ars, P. (2025). Uncertainty-Aware Reinforcement Learning Agents for Noisy Environments. In 2025 tenth International Conference on Information Technology Trends (ITT). IEEE. doi:10.1109/ITT69610.2025.11352934 https://hdl.handle.net/2268/337557

The most cited

1 citations (OpenCitations)

Singh, A., De Schepper, T., Mets, K., Hellinckx, P., Oramas, J., & Latré, S. (2022). Task Independent Capsule-Based Agents for Deep Q-Learning. In Artificial Intelligence and Machine Learning. Switzerland: Springer. doi:10.1007/978-3-030-93842-0_4 https://hdl.handle.net/2268/288478

Most significant publications selected by the Author

Singh, A., de Schepper, T., Mets, K., Hellinckx, P., Oramas, J., & Latré, S. (2022). Deep Set Conditioned Latent Representations for Action Recognition. In VISIGRAPP 2022. Switzerland: Springer Nature Computer Science journal. doi:10.5220/0010838400003124
Peer reviewed

Singh, A., De Schepper, T., Mets, K., Hellinckx, P., Oramas, J., & Latré, S. (2022). Task Independent Capsule-Based Agents for Deep Q-Learning. In Artificial Intelligence and Machine Learning. Switzerland: Springer. doi:10.1007/978-3-030-93842-0_4
Peer reviewed


Singh, A. (2026). Uncertainty Quantification with Machine Learning and Reinforcement Learning for Business Decision-Making [Doctoral thesis, ULiège - University of Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/343707

Singh, A., Ittoo, A., Vandomme, E., & Ars, P. (2025). Uncertainty-Aware Reinforcement Learning Agents for Noisy Environments. In 2025 tenth International Conference on Information Technology Trends (ITT). IEEE. doi:10.1109/ITT69610.2025.11352934
Peer reviewed

Singh, A., Ittoo, A., Ars, P., & Vandomme, E. (2025). Multi-channel multi-model customer conversion prediction in the insurance domain. In A. Taghipour, New Perspectives and Paradigms in Applied Economics and Business: Select Proceedings of the 9th International Conference on Applied Economics and Business, Paris, France, 2025. Switzerland: Springer Cham.
Peer reviewed

Singh, A., Ittoo, A., Ars, P., & Vandomme, E. (20 May 2025). Beyond Yes or No: Making Reliable Decisions and improving personalized customer service (Poster) [Poster presentation]. Research day, Liege, Belgium.

Singh, A., Ittoo, A., Pierre, A., & Vandomme, E. (29 January 2025). Conformal Prediction: Calibrated Decision-Making [Paper presentation]. Joint ORBEL - NGB conference on Operations Research, Maastricht, Netherlands.

Singh, A., Ittoo, A., & Ars, P. (2024). Beyond Yes or No: Making Reliable Decisions and improving personalized customer service. doi:10.13140/RG.2.2.24670.75843https://orbi.uliege.be/handle/2268/328138

Singh, A. (16 May 2024). Heterogeneous Ensemble for Uncertainty Quantification (HEUQ) in churn management [Paper presentation]. Research in the Age of AI, Liege, Belgium.

Singh, A., de Schepper, T., Mets, K., Hellinckx, P., Oramas, J., & Latré, S. (2022). Deep Set Conditioned Latent Representations for Action Recognition. In VISIGRAPP 2022. Switzerland: Springer Nature Computer Science journal. doi:10.5220/0010838400003124
Peer reviewed

Singh, A., De Schepper, T., Mets, K., Hellinckx, P., Oramas, J., & Latré, S. (2022). Task Independent Capsule-Based Agents for Deep Q-Learning. In Artificial Intelligence and Machine Learning. Switzerland: Springer. doi:10.1007/978-3-030-93842-0_4
Peer reviewed

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