Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Hajiesmaili, Mohammad"'
Autor:
Daneshvaramoli, Mohammadreza, Karisani, Helia, Lechowicz, Adam, Sun, Bo, Musco, Cameron, Hajiesmaili, Mohammad
In the online knapsack problem, the goal is to pack items arriving online with different values and weights into a capacity-limited knapsack to maximize the total value of the accepted items. We study \textit{learning-augmented} algorithms for this p
Externí odkaz:
http://arxiv.org/abs/2406.18752
Autor:
Liu, Xutong, Wang, Siwei, Zuo, Jinhang, Zhong, Han, Wang, Xuchuang, Wang, Zhiyong, Li, Shuai, Hajiesmaili, Mohammad, Lui, John C. S., Chen, Wei
We introduce a novel framework of combinatorial multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMAB-MT), where the outcome of each arm is a $d$-dimensional multivariant random variable and the feedback follows a g
Externí odkaz:
http://arxiv.org/abs/2406.01386
Autor:
Bostandoost, Roozbeh, Lechowicz, Adam, Hanafy, Walid A., Bashir, Noman, Shenoy, Prashant, Hajiesmaili, Mohammad
Motivated by an imperative to reduce the carbon emissions of cloud data centers, this paper studies the online carbon-aware resource scaling problem with unknown job lengths (OCSU) and applies it to carbon-aware resource scaling for executing computi
Externí odkaz:
http://arxiv.org/abs/2404.15211
Autor:
Lechowicz, Adam, Christianson, Nicolas, Sun, Bo, Bashir, Noman, Hajiesmaili, Mohammad, Wierman, Adam, Shenoy, Prashant
We introduce and study a family of online metric problems with long-term constraints. In these problems, an online player makes decisions $\mathbf{x}_t$ in a metric space $(X,d)$ to simultaneously minimize their hitting cost $f_t(\mathbf{x}_t)$ and s
Externí odkaz:
http://arxiv.org/abs/2402.14012
We present the first learning-augmented data structure for implementing dictionaries with optimal consistency and robustness. Our data structure, named RobustSL, is a skip list augmented by predictions of access frequencies of elements in a data sequ
Externí odkaz:
http://arxiv.org/abs/2402.09687
Ridesharing services have revolutionized personal mobility, offering convenient on-demand transportation anytime. While early proponents of ridesharing suggested that these services would reduce the overall carbon emissions of the transportation sect
Externí odkaz:
http://arxiv.org/abs/2402.01644
Autor:
Zuo, Jinhang, Zhang, Zhiyao, Wang, Xuchuang, Chen, Cheng, Li, Shuai, Lui, John C. S., Hajiesmaili, Mohammad, Wierman, Adam
Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game. We study latent vulnerabilities exposed by this collaboration and consider adversarial attacks on a few age
Externí odkaz:
http://arxiv.org/abs/2311.01698
Autor:
Lechowicz, Adam, Christianson, Nicolas, Sun, Bo, Bashir, Noman, Hajiesmaili, Mohammad, Wierman, Adam, Shenoy, Prashant
We introduce and study online conversion with switching costs, a family of online problems that capture emerging problems at the intersection of energy and sustainability. In this problem, an online player attempts to purchase (alternatively, sell) f
Externí odkaz:
http://arxiv.org/abs/2310.20598
Autor:
Sun, Bo, Huang, Jerry, Christianson, Nicolas, Hajiesmaili, Mohammad, Wierman, Adam, Boutaba, Raouf
The burgeoning field of algorithms with predictions studies the problem of using possibly imperfect machine learning predictions to improve online algorithm performance. While nearly all existing algorithms in this framework make no assumptions on pr
Externí odkaz:
http://arxiv.org/abs/2310.11558
Autor:
Wang, Lingdong, Singh, Simran, Chakareski, Jacob, Hajiesmaili, Mohammad, Sitaraman, Ramesh K.
Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in improving the quality of degraded videos through deep learning. Neural-E
Externí odkaz:
http://arxiv.org/abs/2310.09920