Finite-particles rates for drifting models @SimonsInstitute

Uploaded August 2026 | Updated September 2026, 1 week ago

Krishna Balasubramanian (UC Davis)
https://simons.berkeley.edu/talks/krishna-balasubramanian-uc-davis-2026-08-03
Diffusion Generative Modeling: Progress and Next Steps

We view drifting from a Wasserstein gradient flow (WGF) perspective and propose a conservative method for one-step generative modeling, replacing the original displacement-based velocity with a KDE-gradient field given by the difference between kernel-smoothed data and model scores. Using a joint-entropy identity, we establish continuous-time finite-particle bounds on R^d, including a root residual-velocity rate of N^{-1/(d+4)} under bandwidth-uniform quadrature regularity. We also analyze the original non-conservative Laplace-kernel method through a sharp companion-kernel decomposition and show how the resulting residual-velocity bounds yield explicit one-step generation guarantees in terms of the drift size.

$Finite-particles rates for drifting models Krishna Balasubramanian (UC Davis) https://simons.berkeley.edu/talks/krishna-balasubramanian-uc-davis-2026-08-03 Diffusion Generative Modeling: Progress and Next Steps We view drifting from a Wasserstein gradient flow (WGF) perspective and propose a conservative method for one-step generative modeling, replacing the original displacement-based velocity with a KDE-gradient field given by the difference between kernel-smoothed data and model scores. Using a joint-entropy identity, we establish continuous-time finite-particle bounds on R^d, including a root residual-velocity rate of N^{-1/(d+4)} under bandwidth-uniform quadrature regularity. We also analyze the original non-conservative Laplace-kernel method through a sharp companion-kernel decomposition and show how the resulting residual-velocity bounds yield explicit one-step generation guarantees in terms of the drift size.$

Tradeoffs and Limitations in Algorithmic Fairness

On the interplay of accuracy and fairness in computational healthcare

Systems A/B/M : Lessons on Autonomous Learning from Cognitive Science

Reconciling Biological and Social Research in Autism | Distinguished Lecture

Nest construction by weaver ants: Cognition without a brain

Can Big Data Help Children Thrive? National Implementation of Research-Based Tools

Building a Quantum Computer with QLDPC Codes

Diffusion in RL and robotics: how expressive policies changed how we use continuous actions