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pro vyhledávání: '"Sel, Bilgehan"'
Autor:
Sel, Bilgehan
Solving a sequence of high-dimensional, nonconvex, but potentially similar optimization problems poses a significant computational challenge in various engineering applications. This thesis presents the first meta-learning framework that leverages th
Externí odkaz:
https://hdl.handle.net/10919/119325
Publikováno v:
ICLR 2023
Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has not been adequately addressed in the existing works, making their applic
Externí odkaz:
http://arxiv.org/abs/2405.16601
In numerous reinforcement learning (RL) problems involving safety-critical systems, a key challenge lies in balancing multiple objectives while simultaneously meeting all stringent safety constraints. To tackle this issue, we propose a primal-based f
Externí odkaz:
http://arxiv.org/abs/2405.16390
Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant challenges in the domain of moral reasoning and ethical decision-making,
Externí odkaz:
http://arxiv.org/abs/2405.12933
Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications. Nevertheless, managing the trade-off between reward and safety during exploration presents a significant challenge. Improving reward performa
Externí odkaz:
http://arxiv.org/abs/2405.01677
Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to external modi operandi involving halting, modifying, and then resuming the generation process to boost Large Language Models' (LLMs) reasoning capacities. Due to
Externí odkaz:
http://arxiv.org/abs/2308.10379
This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs). We focus on customizable optimization problems that necessitate repeated solving with slight variations in model
Externí odkaz:
http://arxiv.org/abs/2308.10380
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension. The main results are: \emph{(1)} the class of solution functions of linear programming (LP) and quadratic programmin
Externí odkaz:
http://arxiv.org/abs/2212.01314
Publikováno v:
In IFAC PapersOnLine 2023 56(2):9336-9341
Publikováno v:
TÜRK TURİZM ARAŞTIRMALARI DERGİSİ / JOURNAL OF TURKISH TOURISM RESEARCH. 4(3):1986-2002
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=888907