6 papers · 1 filter
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…
Stability of the Kim--Milman flow map
Sinho Chewi, Aram-Alexandre Pooladian, Matthew S. Zhang
In this short note, we characterize stability of the Kim--Milman flow map -- also known as the probability flow ODE -- with respect to variations in the target measure in relative…
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…
Shifted Composition IV: Toward Ballistic Acceleration for Log-Concave Sampling
Jason M. Altschuler, Sinho Chewi, Matthew S. Zhang
Acceleration is a celebrated cornerstone of convex optimization, enabling gradient-based algorithms to converge sublinearly in the condition number. A major open question is whethe…