Lightning Talks! @SimonsInstitute

Uploaded May 2026 | Updated September 2026, 1 week ago

Speaker List:

Anupam Gupta

Kevin Stangl

Kavya Ramachandran

Han Shao

Naren Manoj

Jamie Morgenstern

Xinyuan Cao

Lev Reyzin

Adam Kalai

Siddharth Prasad

Pravesh Kothari

The Role of TCS in Modern Machine Learning

Tutorial: Federated Optimization, Part I

Tutorial: Federated Optimization, Part II

Optimal Inference Schedules for Masked Diffusion Models

Astral Space: Convex Analysis at Infinity

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

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