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pro vyhledávání: '"KUMAR, NIRAJ"'
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data to maintain model accuracy. This is particularly important in applicat
Externí odkaz:
http://arxiv.org/abs/2406.12008
Autor:
Heredge, Jamie, Kumar, Niraj, Herman, Dylan, Chakrabarti, Shouvanik, Yalovetzky, Romina, Sureshbabu, Shree Hari, Li, Changhao, Pistoia, Marco
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collaborative learning. Motivated by the increasing success of recovering in
Externí odkaz:
http://arxiv.org/abs/2405.08801
This work studies fixed priority (FP) scheduling of real-time jobs with end-to-end deadlines in a distributed system. Specifically, given a multi-stage pipeline with multiple heterogeneous resources of the same type at each stage, the problem is to a
Externí odkaz:
http://arxiv.org/abs/2403.13411
Autor:
Prakash, Anupam, Sun, Yue, Chakrabarti, Shouvanik, Che, Charlie, Dandapani, Aditi, Herman, Dylan, Kumar, Niraj, Sureshbabu, Shree Hari, Wood, Ben, Kerenidis, Iordanis, Pistoia, Marco
We consider the problem of pricing discretely monitored Asian options over $T$ monitoring points where the underlying asset is modeled by a geometric Brownian motion. We provide two quantum algorithms with complexity poly-logarithmic in $T$ and polyn
Externí odkaz:
http://arxiv.org/abs/2402.10132
Publikováno v:
Quantum Science and Technology, Volume 9, Number 3, 2024
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum
Externí odkaz:
http://arxiv.org/abs/2312.04447
Autor:
Li, Changhao, Li, Boning, Amer, Omar, Shaydulin, Ruslan, Chakrabarti, Shouvanik, Wang, Guoqing, Xu, Haowei, Tang, Hao, Schoch, Isidor, Kumar, Niraj, Lim, Charles, Li, Ju, Cappellaro, Paola, Pistoia, Marco
Publikováno v:
Phys. Rev. Lett. 133, 120602 (2024)
Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecti
Externí odkaz:
http://arxiv.org/abs/2310.12893
Autor:
Kumar, Niraj, Heredge, Jamie, Li, Changhao, Eloul, Shaltiel, Sureshbabu, Shree Hari, Pistoia, Marco
Federated learning has emerged as a viable distributed solution to train machine learning models without the actual need to share data with the central aggregator. However, standard neural network-based federated learning models have been shown to be
Externí odkaz:
http://arxiv.org/abs/2309.13002