Zobrazeno 1 - 10
of 19 496
pro vyhledávání: '"TALEBI, A."'
Autor:
Huang, Meng-Zi, Fabritius, Philipp, Mohan, Jeffrey, Talebi, Mohsen, Wili, Simon, Esslinger, Tilman
Limited transport occurs in various systems when microscopic details give way to fundamental principles, ranging from quantized conductance for fermions in one dimension to quantum-limited sound and spin diffusivity in strongly interacting Fermi gase
Externí odkaz:
http://arxiv.org/abs/2412.08525
Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in personalized services. Previous work mainly focuses on two trust models of DP: the central model, where a ce
Externí odkaz:
http://arxiv.org/abs/2411.11647
We present the design, development, and experimental validation of BlueME, a compact magnetoelectric (ME) antenna array system for underwater robot-to-robot communication. BlueME employs ME antennas operating at their natural mechanical resonance fre
Externí odkaz:
http://arxiv.org/abs/2411.09241
The Sherrington-Kirkpatrick spin-glass model used the replica symmetry method to find the phase transition of the system. In 1979-1980, Parisi proposed a solution based on replica symmetry breaking (RSB), which allowed him to identify the underlying
Externí odkaz:
http://arxiv.org/abs/2411.04567
To address deviations from expected performance in stochastic systems, we propose a risk-sensitive control synthesis method to minimize certain risk measures over the limiting stationary distribution. Specifically, we extend Worst-case Conditional Va
Externí odkaz:
http://arxiv.org/abs/2410.17581
Autor:
Talebi, Shahriar, Li, Na
In stochastic systems, risk-sensitive control balances performance with resilience to less likely events. Although existing methods rely on finite-horizon risk criteria, this paper introduces \textit{limiting-risk criteria} that capture long-term cum
Externí odkaz:
http://arxiv.org/abs/2409.10767
This work studies offline Reinforcement Learning (RL) in a class of non-Markovian environments called Regular Decision Processes (RDPs). In RDPs, the unknown dependency of future observations and rewards from the past interactions can be captured by
Externí odkaz:
http://arxiv.org/abs/2409.02747
Web-based Large Language Model (LLM) services have been widely adopted and have become an integral part of our Internet experience. Third-party plugins enhance the functionalities of LLM by enabling access to real-world data and services. However, th
Externí odkaz:
http://arxiv.org/abs/2408.07004
Autor:
Talebi, Amirreza
We study opinion dynamics over a directed multilayer network. In particular, we consider networks in which the impact of neighbors of agents on their opinions is proportional to their in-degree. Agents update their opinions over time to coordinate wi
Externí odkaz:
http://arxiv.org/abs/2407.17749
Autor:
Talebi, Amirreza
Discrete choice models (DCMs) have been widely utilized in various scientific fields, especially economics, for many years. These models consider a stochastic environment influencing each decision maker's choices. Extensive research has shown that th
Externí odkaz:
http://arxiv.org/abs/2407.17014