Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Korenkevych, Dmytro"'
Autor:
Zhu, Zheqing, Braz, Rodrigo de Salvo, Bhandari, Jalaj, Jiang, Daniel, Wan, Yi, Efroni, Yonathan, Wang, Liyuan, Xu, Ruiyang, Guo, Hongbo, Nikulkov, Alex, Korenkevych, Dmytro, Dogan, Urun, Cheng, Frank, Wu, Zheng, Xu, Wanqiao
Publikováno v:
Journal of Machine Learning Research, 2024
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performant RL policy requires a system designed to address several important ch
Externí odkaz:
http://arxiv.org/abs/2312.03814
Autor:
Korenkevych, Dmytro, Cheng, Frank, Balakir, Artsiom, Nikulkov, Alex, Gao, Lingnan, Cen, Zhihao, Xu, Zuobing, Zhu, Zheqing
The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constraint. Thus, advertising platforms typically provide automated agents to
Externí odkaz:
http://arxiv.org/abs/2310.09426
Autor:
Xu, Ruiyang, Bhandari, Jalaj, Korenkevych, Dmytro, Liu, Fan, He, Yuchen, Nikulkov, Alex, Zhu, Zheqing
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on use
Externí odkaz:
http://arxiv.org/abs/2305.13747
Reinforcement learning algorithms rely on exploration to discover new behaviors, which is typically achieved by following a stochastic policy. In continuous control tasks, policies with a Gaussian distribution have been widely adopted. Gaussian explo
Externí odkaz:
http://arxiv.org/abs/1903.11524
Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these result
Externí odkaz:
http://arxiv.org/abs/1809.07731
Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption i
Externí odkaz:
http://arxiv.org/abs/1803.07067
Autor:
Korenkevych, Dmytro, Xue, Yanbo, Bian, Zhengbing, Chudak, Fabian, Macready, William G., Rolfe, Jason, Andriyash, Evgeny
Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA relies on quantum, rather than thermal, effects to explore complex sear
Externí odkaz:
http://arxiv.org/abs/1611.04528
Autor:
Korenkevych, Dmytro, Ozrazgat-Baslanti, Tezcan, Thottakkara, Paul, Hobson, Charles E, Pardalos, Panos, Momcilovic, Petar, Bihorac, Azra
Calculate mortality risk that accounts for both severity and recovery of postoperative kidney dysfunction using the pattern of longitudinal change in creatinine.Although the importance of renal recovery after acute kidney injury (AKI) is increasingly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::052aa32701f88f11d64bb4bfbee8a984
https://europepmc.org/articles/PMC4829495/
https://europepmc.org/articles/PMC4829495/
Akademický článek
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Publikováno v:
Models, Algorithms & Technologies for Network Analysis; 2013, p117-127, 11p