From the 1 of 7 linked papers with an AI index.
7 papers
Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo
Siddharth Mitra, Vishwak Srinivasan, Xiuyuan Wang +1
The paper proves that Randomized Hamiltonian Monte Carlo mixes faster for log‑concave distributions, providing exponential KL convergence rates and explicit total integration time…
Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time
Xiuyuan Wang, Vishwak Srinivasan, Qiang Fu +3
We develop Hamiltonian dynamics-based algorithms for smooth convex optimization that achieve accelerated rates of convergence. By exploiting contraction of averaged Hamiltonian flo…
Convergence of the Inexact Langevin Algorithm in KL Divergence with Application to Score-based Generative Models
Kaylee Yingxi Yang, Andre Wibisono
Motivated by the increasingly popular Score-based Generative Modeling (SGM), we study the Inexact Langevin Dynamics (ILD) and Inexact Langevin Algorithm (ILA) where a score functio…
Hamiltonian Descent Algorithms for Optimization: Accelerated Rates via Randomized Integration Time
Qiang Fu, Andre Wibisono
We study the Hamiltonian flow for optimization (HF-opt), which simulates the Hamiltonian dynamics for some integration time and resets the velocity to to decrease the objective…
Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality
Andre Wibisono
We study the mixing time guarantee for sampling in relative Fisher information via the Proximal Sampler algorithm, which is an approximate proximal discretization of the Langevin d…
Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play
John Lazarsfeld, Georgios Piliouras, Ryann Sim +1
This paper investigates the sublinear regret guarantees of two non-no-regret algorithms in zero-sum games: Fictitious Play, and Online Gradient Descent with constant stepsizes. In…