Zobrazeno 1 - 10
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pro vyhledávání: '"Triantafyllou, A"'
We address the challenge of explaining counterfactual outcomes in multi-agent Markov decision processes. In particular, we aim to explain the total counterfactual effect of an agent's action on the outcome of a realized scenario through its influence
Externí odkaz:
http://arxiv.org/abs/2410.12539
Autor:
Sun, Chuanhao, Triantafyllou, Thanos, Makris, Anthos, Drmač, Maja, Xu, Kai, Mai, Luo, Marina, Mahesh K.
View synthesis using Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) has demonstrated impressive fidelity in rendering real-world scenarios. However, practical methods for accurate and efficient epistemic Uncertainty Quantification (UQ) in
Externí odkaz:
http://arxiv.org/abs/2410.05468
A novel Material Point Method (MPM) is introduced for addressing frictional contact problems. In contrast to the standard multi-velocity field approach, this method employs a penalty method to evaluate contact forces at the discretised boundaries of
Externí odkaz:
http://arxiv.org/abs/2403.13534
When Reinforcement Learning (RL) agents are deployed in practice, they might impact their environment and change its dynamics. We propose a new framework to model this phenomenon, where the current environment depends on the deployed policy as well a
Externí odkaz:
http://arxiv.org/abs/2402.09838
This work introduces a framework to address the computational complexity inherent in Mixed-Integer Programming (MIP) models by harnessing the potential of deep learning. By employing deep learning, we construct problem-specific heuristics that identi
Externí odkaz:
http://arxiv.org/abs/2401.09556
Establishing causal relationships between actions and outcomes is fundamental for accountable multi-agent decision-making. However, interpreting and quantifying agents' contributions to such relationships pose significant challenges. These challenges
Externí odkaz:
http://arxiv.org/abs/2310.11334
Autor:
M. A. Salgado-Gálvez, M. Ordaz, B. Huerta, O. Garay, C. Avelar, E. Fagà, M. Kohrangi, P. Ceresa, G. Triantafyllou, U. T. Begaliev
Publikováno v:
Natural Hazards and Earth System Sciences, Vol 24, Pp 3851-3868 (2024)
A fully probabilistic earthquake risk model was developed for five countries in Central Asia, providing updated earthquake loss estimates with a higher level of detail on all components with respect to previous studies in the region. Additionally, a
Externí odkaz:
https://doaj.org/article/34ac11336ac84913ba4a872f9fed908e
Autor:
Zoi Papalamprakopoulou, Elisavet Ntagianta, Vasiliki Triantafyllou, George Kalamitsis, Arpan Dharia, Suzanne S. Dickerson, Angelos Hatzakis, Andrew H. Talal
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background People who use drugs (PWUD) often face restricted healthcare access despite their heightened healthcare needs. Factors such as stigma, mistrust of the healthcare system, competing priorities, and geographical barriers pose signifi
Externí odkaz:
https://doaj.org/article/d89eef67cf45453b9383060016490f11
Publikováno v:
International Journal of Quality & Reliability Management, 2024, Vol. 41, Issue 9, pp. 2229-2231.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJQRM-10-2024-457
Autor:
Ioannis S. Triantafyllou
Publikováno v:
Stats, Vol 7, Iss 3, Pp 906-923 (2024)
In the present work, we study the number of working units of a consecutive-type structure at a specific time point under the condition that the system’s failure has not been observed yet. The main results of this paper offer some closed formulae fo
Externí odkaz:
https://doaj.org/article/fed91604b1c04afabf179e71d939a2a5