4 citations · 5 across the 10 of their papers we have counts for
11 papers
Uniform-in-time relative entropy estimates for Kac's approximation of the Landau equation
Xuanrui Feng, Chenguang Liu, Zhenfu Wang
We prove uniform-in-time quantitative relative entropy estimates for Kac's particle approximation of the three-dimensional spatially homogeneous Landau equation for Maxwellian mole…
Quantitative propagation of chaos for the Boltzmann equation with moderately soft potentials
Chenguang Liu, Liping Xu, An Zhang
We study the Kac particle system associated with the spatially homogeneous Boltzmann equation with non-cutoff collision kernels in the moderately soft potential regime . We…
Scaling limits for supercritical nearly unstable Hawkes processes
Chenguang Liu, Liping Xu, An Zhang
In this paper, we investigate the asymptotic behavior of nearly unstable Hawkes processes whose regression kernel has norm strictly greater than one and close to one as time…
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…
Convergence rates for Backward SDEs driven by Lévy processes
Chenguang Liu, Antonis Papapantoleon, Alexandros Saplaouras
We consider Lévy processes that are approximated by compound Poisson processes and, correspondingly, BSDEs driven by Lévy processes that are approximated by BSDEs driven by their c…
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…
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…
Rate of convergence of the Kac-like particle system
Chenguang Liu, Liping Xu
In this paper, we consider the Kac stochastic particle system associated to the spatially homogeneous Boltzmann equation for true hard potentials. We establish a rate of propagatio…
Central limit theorem for a partially observed interacting system of Hawkes processes
Chenguang Liu
We observe the actions of a sub-sample of individuals up to time for some large . We model the relationships of individuals by i.i.d. Bernoulli()-random vari…
Statistical inference for a partially observed interacting system of Hawkes processes
Chenguang Liu
We observe the actions of a sub-sample of individuals up to time for some large . We model the relationships of individuals by i.i.d. Bernoulli()-random variabl…