Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning
The privacy-preserving federated learning for vertically partitioned (VP) data has shown
promising results as the solution of the emerging multiparty joint modeling application, in …
promising results as the solution of the emerging multiparty joint modeling application, in …
PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization
In this paper, we propose a novel stochastic gradient estimator—ProbAbilistic Gradient
Estimator (PAGE)—for nonconvex optimization. PAGE is easy to implement as it is designed …
Estimator (PAGE)—for nonconvex optimization. PAGE is easy to implement as it is designed …
[图书][B] First-order and stochastic optimization methods for machine learning
G Lan - 2020 - Springer
Since its beginning, optimization has played a vital role in data science. The analysis and
solution methods for many statistical and machine learning models rely on optimization. The …
solution methods for many statistical and machine learning models rely on optimization. The …
Spiderboost and momentum: Faster variance reduction algorithms
SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms,
and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in …
and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in …
Convex optimization algorithms in medical image reconstruction—in the age of AI
The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …
algorithms, which are often applications or adaptations of convex optimization algorithms …
Stochastic first-order methods for convex and nonconvex functional constrained optimization
Functional constrained optimization is becoming more and more important in machine
learning and operations research. Such problems have potential applications in risk-averse …
learning and operations research. Such problems have potential applications in risk-averse …
HoloFed: Environment-adaptive positioning via multi-band reconfigurable holographic surfaces and federated learning
Positioning is an essential service for various applications and is expected to be integrated
with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular …
with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular …
A unified convergence analysis for shuffling-type gradient methods
In this paper, we propose a unified convergence analysis for a class of generic shuffling-type
gradient methods for solving finite-sum optimization problems. Our analysis works with any …
gradient methods for solving finite-sum optimization problems. Our analysis works with any …
A novel convergence analysis for algorithms of the adam family
Since its invention in 2014, the Adam optimizer has received tremendous attention. On one
hand, it has been widely used in deep learning and many variants have been proposed …
hand, it has been widely used in deep learning and many variants have been proposed …
Variance-reduced clipping for non-convex optimization
Gradient clipping is a standard training technique used in deep learning applications such
as large-scale language modeling to mitigate exploding gradients. Recent experimental …
as large-scale language modeling to mitigate exploding gradients. Recent experimental …