Vertical federated learning (VFL) attracts increasing attention due to the emerging demands
of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL …

Recently, there is a growing attention on applying deep reinforcement learning (DRL) to
solve the 3-D bin packing problem (3-D BPP). However, due to the relatively less informative …

Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-
organizational (eg, different corporations, companies and organizations) collaborative …

Adaptive gradient methods have shown excellent performances for solving many machine
learning problems. Although multiple adaptive gradient methods were recently studied, they …

Stochastic Approximation (SA) is a classical algorithm that has had since the early days a
huge impact on signal processing, and nowadays on machine learning, due to the necessity …

Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each
example, where class variables from different class spaces characterize semantics of the …

Vertical federated learning (VFL) attracts increasing attention due to the emerging demands
of multi-party collaborative modeling and concerns of privacy leakage. A complete list of …

The extreme success of stochastic optimization (SO) in large-scale machine learning
problems, information retrieval, bioinformatics, etc., has been widely reported, especially in …

In a class of large-scale distributed optimization, the calculation of RELM based on the
Moore–Penrose inverse matrix is prohibitively expensive, which hinders the formulation of a …

Measurement of forest biomass is a time-, money-, and labor-consuming activity. Develo**
methodological approaches for quantifying biomass in natural ecosystems has motivated …