27 citations · 45 across the 4 of their papers we have counts for
5 papers
OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale
Dihong Jiang, Ruoqi Cao, Zhiyuan Dang +8
While traditional tree-based ensemble methods have long dominated tabular tasks, deep neural networks and emerging foundation models have challenged this primacy, yet no consensus…
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
Qingsong Zhang, Bin Gu, Zhiyuan Dang +2
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 o…
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Qingsong Zhang, Bin Gu, Cheng Deng +4
Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative l…
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Qingsong Zhang, Bin Gu, Cheng Deng +1
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 a…
Faster Stochastic Quasi-Newton Methods
Qingsong Zhang, Feihu Huang, Cheng Deng +1
Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machin…