Zobrazeno 1 - 10
of 2 480
pro vyhledávání: '"Madduri, A."'
The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges
Externí odkaz:
http://arxiv.org/abs/2409.19756
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is proprietary, confidential, and distributed, FL has become a promising app
Externí odkaz:
http://arxiv.org/abs/2409.11585
Autor:
Mishra, Ashirbad, Dey, Soumik, Wu, Marshall, Zhao, Jinyu, Yu, He, Ni, Kaichen, Li, Binbin, Madduri, Kamesh
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendations is Extreme Multi-Label Classification (XMC), which involves taggin
Externí odkaz:
http://arxiv.org/abs/2409.03140
Keyphrase Recommendation has been a pivotal problem in advertising and e-commerce where advertisers/sellers are recommended keyphrases (search queries) to bid on to increase their sales. It is a challenging task due to the plethora of items shown on
Externí odkaz:
http://arxiv.org/abs/2407.20462
"Garbage In Garbage Out" is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low-quality, biased data are often ineffective. Computer scienti
Externí odkaz:
http://arxiv.org/abs/2406.19256
Autor:
Li, Zilinghan, He, Shilan, Chaturvedi, Pranshu, Kindratenko, Volodymyr, Huerta, Eliu A, Kim, Kibaek, Madduri, Ravi
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models. In this paper, we elaborate o
Externí odkaz:
http://arxiv.org/abs/2402.12271
Autor:
Hoang, Trung-Hieu, Fuhrman, Jordan, Madduri, Ravi, Li, Miao, Chaturvedi, Pranshu, Li, Zilinghan, Kim, Kibaek, Ryu, Minseok, Chard, Ryan, Huerta, E. A., Giger, Maryellen
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is
Externí odkaz:
http://arxiv.org/abs/2312.08701
Autor:
Li, Zilinghan, Chaturvedi, Pranshu, He, Shilan, Chen, Han, Singh, Gagandeep, Kindratenko, Volodymyr, Huerta, E. A., Kim, Kibaek, Madduri, Ravi
Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralize
Externí odkaz:
http://arxiv.org/abs/2309.14675
Autor:
Li, Zilinghan, He, Shilan, Chaturvedi, Pranshu, Hoang, Trung-Hieu, Ryu, Minseok, Huerta, E. A., Kindratenko, Volodymyr, Fuhrman, Jordan, Giger, Maryellen, Chard, Ryan, Kim, Kibaek, Madduri, Ravi
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the a
Externí odkaz:
http://arxiv.org/abs/2308.08786
The multilevel heuristic is the dominant strategy for high-quality sequential and parallel graph partitioning. Partition refinement is a key step of multilevel graph partitioning. In this work, we present Jet, a new parallel algorithm for partition r
Externí odkaz:
http://arxiv.org/abs/2304.13194