88 citations · 482 across the 90 of their papers we have counts for
14 papers · 1 filter
Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics
Zonghan Yang, Peng Li, Tianyu Pang +1
Deep equilibrium (DEQ) models replace the multiple-layer stacking of conventional deep networks with a fixed-point iteration of a single-layer transformation. Having been demonstra…
A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
Zonghan Yang, Tianyu Pang, Yang Liu
Deep equilibrium models (DEQs) refrain from the traditional layer-stacking paradigm and turn to find the fixed point of a single layer. DEQs have achieved promising performance on…
RSRM: Reinforcement Symbolic Regression Machine
Yilong Xu, Yang Liu, Hao Sun
In nature, the behaviors of many complex systems can be described by parsimonious math equations. Automatically distilling these equations from limited data is cast as a symbolic r…
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts
Kun Jin, Tongxin Yin, Zhongzhu Chen +4
We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distribu…
Federated Learning without Full Labels: A Survey
Yilun Jin, Yang Liu, Kai Chen +1
Data privacy has become an increasingly important concern in real-world big data applications such as machine learning. To address the problem, federated learning (FL) has been a p…
Mutual Information Regularization for Vertical Federated Learning
Tianyuan Zou, Yang Liu, Ya-Qin Zhang
Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting data privacy and safety. However, previous works s…