5 papers
Sublinear iterations can suffice even for DDPMs
Matthew S. Zhang, Stephen Huan, Jerry Huang +3
SDE-based methods such as denoising diffusion probabilistic models (DDPMs) have shown remarkable success in real-world sample generation tasks. Prior analyses of DDPMs have been fo…
Perspectives on Stochastic Localization
Bobby Shi, Kevin Tian, Matthew S. Zhang
We survey different perspectives on the stochastic localization process of Eldan, a powerful construction that has had many exciting recent applications in high-dimensional probabi…
Graph Random Features for Scalable Gaussian Processes
Matthew Zhang, Jihao Andreas Lin, Krzysztof Choromanski +3
We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input space…
Analysis of Langevin midpoint methods using an anticipative Girsanov theorem
Matthew S. Zhang
We introduce a new method for analyzing midpoint discretizations of stochastic differential equations (SDEs), which are frequently used in Markov chain Monte Carlo (MCMC) methods f…
Uniform-in- log-Sobolev inequality for the mean-field Langevin dynamics with convex energy
Sinho Chewi, Atsushi Nitanda, Matthew S. Zhang
We establish a log-Sobolev inequality for the stationary distribution of mean-field Langevin dynamics with a constant that is independent of the number of particles . Our proof…