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pro vyhledávání: '"Ness, B"'
Multi-objective Markov Decision Processes (MDPs) are receiving increasing attention, as real-world decision-making problems often involve conflicting objectives that cannot be addressed by a single-objective MDP. The Pareto front identifies the set o
Externí odkaz:
http://arxiv.org/abs/2410.15557
Continual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a major issue in CL, as the model adapts to new tasks. The Mi
Externí odkaz:
http://arxiv.org/abs/2406.16437
In this paper, we study a sampling problem where a source takes samples from a Wiener process and transmits them through a wireless channel to a remote estimator. Due to channel fading, interference, and potential collisions, the packet transmissions
Externí odkaz:
http://arxiv.org/abs/2310.11037
We study a class of scheduling problems, where each job is divided into a batch of unit-size tasks and these tasks can be executed in parallel on multiple servers with New-Better-than-Used (NBU) service time distributions. While many delay optimality
Externí odkaz:
http://arxiv.org/abs/2309.16880
In this paper, we consider a task offloading problem in a multi-access edge computing (MEC) network, in which edge users can either use their local processing unit to compute their tasks or offload their tasks to a nearby edge server through multiple
Externí odkaz:
http://arxiv.org/abs/2308.06647
Transfer learning is a useful technique for achieving improved performance and reducing training costs by leveraging the knowledge gained from source tasks and applying it to target tasks. Assessing the effectiveness of transfer learning relies on un
Externí odkaz:
http://arxiv.org/abs/2306.04901
Fairness plays a crucial role in various multi-agent systems (e.g., communication networks, financial markets, etc.). Many multi-agent dynamical interactions can be cast as Markov Decision Processes (MDPs). While existing research has focused on stud
Externí odkaz:
http://arxiv.org/abs/2306.00324
Meta-learning has arisen as a successful method for improving training performance by training over many similar tasks, especially with deep neural networks (DNNs). However, the theoretical understanding of when and why overparameterized models such
Externí odkaz:
http://arxiv.org/abs/2304.04312
In this paper, we consider a status update system, where an access point collects measurements from multiple sensors that monitor a common physical process, fuses them, and transmits the aggregated sample to the destination over an erasure channel. U
Externí odkaz:
http://arxiv.org/abs/2302.13479
Autor:
Qiu, Peiwen, Li, Yining, Liu, Zhuqing, Khanduri, Prashant, Liu, Jia, Shroff, Ness B., Bentley, Elizabeth Serena, Turck, Kurt
Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e.g., multi-agent meta-learning and multi-agent reinforcement learning) over peer-to-peer edge
Externí odkaz:
http://arxiv.org/abs/2212.02376