Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Abyaneh, Amin"'
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, 2024, pp. 15061-15067
Imitation learning presents an effective approach to alleviate the resource-intensive and time-consuming nature of policy learning from scratch in the solution space. Even though the resulting policy can mimic expert demonstrations reliably, it often
Externí odkaz:
http://arxiv.org/abs/2403.04118
Autor:
Abyaneh, Amin, Lin, Hsiu-Chin
Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert's behavior. However, relying solely on the expert's data might lead to unsafe actions when the robot deviates from the demonstrated
Externí odkaz:
http://arxiv.org/abs/2310.20605
Autor:
Abyaneh, Amin, Scherrer, Nino, Schwab, Patrick, Bauer, Stefan, Schölkopf, Bernhard, Mehrjou, Arash
Causal discovery serves a pivotal role in mitigating model uncertainty through recovering the underlying causal mechanisms among variables. In many practical domains, such as healthcare, access to the data gathered by individual entities is limited,
Externí odkaz:
http://arxiv.org/abs/2211.03846
Autor:
Mehrjou, Arash, Soleymani, Ashkan, Abyaneh, Amin, Bhatt, Samir, Schölkopf, Bernhard, Bauer, Stefan
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak. Many existing simulators are based on compar
Externí odkaz:
http://arxiv.org/abs/2103.15561
Akademický článek
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Autor:
Abyaneh, Amin, Scherrer, Nino, Schwab, Patrick, Bauer, Stefan, Schölkopf, Bernhard, Mehrjou, Arash
Existing causal discovery methods typically require the data to be available in a centralized location. However, many practical domains, such as healthcare, limit access to the data gathered by local entities, primarily for privacy and regulatory con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6d00188733272c50200e7539926973b
http://arxiv.org/abs/2211.03846
http://arxiv.org/abs/2211.03846