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of 28
pro vyhledávání: '"Li, Boyue"'
Autor:
Li, Boyue, Chi, Yuejie
Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the r
Externí odkaz:
http://arxiv.org/abs/2305.09896
To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compressio
Externí odkaz:
http://arxiv.org/abs/2206.09888
Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design
Externí odkaz:
http://arxiv.org/abs/2201.13320
Emerging applications in multi-agent environments such as internet-of-things, networked sensing, autonomous systems and federated learning, call for decentralized algorithms for finite-sum optimizations that are resource-efficient in terms of both co
Externí odkaz:
http://arxiv.org/abs/2110.01165
Autor:
Lee, Harlin, Li, Boyue, DeForte, Shelly, Splaingard, Mark, Huang, Yungui, Chi, Yuejie, Linwood, Simon Lin
Publikováno v:
Sci Data 9, 421 (2022)
Despite being crucial to health and quality of life, sleep -- especially pediatric sleep -- is not yet well understood. This is exacerbated by lack of access to sufficient pediatric sleep data with clinical annotation. In order to accelerate research
Externí odkaz:
http://arxiv.org/abs/2102.13284
There is growing interest in large-scale machine learning and optimization over decentralized networks, e.g. in the context of multi-agent learning and federated learning. Due to the imminent need to alleviate the communication burden, the investigat
Externí odkaz:
http://arxiv.org/abs/1909.05844
We study minimax convergence rates of nonparametric density estimation under a large class of loss functions called "adversarial losses", which, besides classical $\mathcal{L}^p$ losses, includes maximum mean discrepancy (MMD), Wasserstein distance,
Externí odkaz:
http://arxiv.org/abs/1805.08836
We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural Networks (RNNs) and Predictive State Representations (PSRs), and inheri
Externí odkaz:
http://arxiv.org/abs/1705.09353
Akademický článek
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Autor:
Li, Boyue
Emerging applications in multi-agent environments such as internet-of-things, networked sensing, large-scale machine learning and federated learning, have attracting increasing attention for decentralized optimization algorithms that are resource eff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf49fafece3d0570effb7b357ec46be2