14 citations · 20 across the 8 of their papers we have counts for
8 papers
How to beat a Bayesian adversary
Zihan Ding, Kexin Jin, Jonas Latz +1
Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model's prediction th…
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm
Lisang Ding, Kexin Jin, Bicheng Ying +2
Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Ra…
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
Yi-Fan Zhang, Xue Wang, Kexin Jin +5
Many recent machine learning tasks focus to develop models that can generalize to unseen distributions. Domain generalization (DG) has become one of the key topics in various field…
Subsampling Error in Stochastic Gradient Langevin Diffusions
Kexin Jin, Chenguang Liu, Jonas Latz
The Stochastic Gradient Langevin Dynamics (SGLD) are popularly used to approximate Bayesian posterior distributions in statistical learning procedures with large-scale data. As opp…
Communication-Efficient Topologies for Decentralized Learning with Consensus Rate
Zhuoqing Song, Weijian Li, Kexin Jin +4
Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server.…
Losing momentum in continuous-time stochastic optimisation
Kexin Jin, Jonas Latz, Chenguang Liu +1
The training of modern machine learning models often consists in solving high-dimensional non-convex optimisation problems that are subject to large-scale data. In this context, mo…
A Continuous-time Stochastic Gradient Descent Method for Continuous Data
Kexin Jin, Jonas Latz, Chenguang Liu +1
Optimization problems with continuous data appear in, e.g., robust machine learning, functional data analysis, and variational inference. Here, the target function is given as an i…
Cancellations of Resonances and Long Time Dynamics of Cubic Schrödinger Equation on
Kexin Jin, Xiao Ma
We prove a vanishing property of the normal form transformation of the 1D cubic nonlinear Schrödinger (NLS) equation with periodic boundary conditions on . We apply this pro…