15 citations · 56 across the 38 of their papers we have counts for
8 papers · 1 filter
Shifted Composition II: Shift Harnack Inequalities and Curvature Upper Bounds
Jason M. Altschuler, Sinho Chewi
We apply the shifted composition rule -- an information-theoretic principle introduced in our earlier work [AC23] -- to establish shift Harnack inequalities for the Langevin diffus…
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian
We develop a theory of finite-dimensional polyhedral subsets over the Wasserstein space and optimization of functionals over them via first-order methods. Our main application is t…
Shifted Composition I: Harnack and Reverse Transport Inequalities
Jason M. Altschuler, Sinho Chewi
We formulate a new information-theoretic principle--the shifted composition rule--which bounds the divergence (e.g., Kullback-Leibler or Rényi) between the laws of two stochastic p…
The probability flow ODE is provably fast
Sitan Chen, Sinho Chewi, Holden Lee +3
We provide the first polynomial-time convergence guarantees for the probability flow ODE implementation (together with a corrector step) of score-based generative modeling. Our ana…
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space
Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi +1
Variational inference (VI) seeks to approximate a target distribution by an element of a tractable family of distributions. Of key interest in statistics and machine learning i…
Query lower bounds for log-concave sampling
Sinho Chewi, Jaume de Dios Pont, Jerry Li +2
Log-concave sampling has witnessed remarkable algorithmic advances in recent years, but the corresponding problem of proving lower bounds for this task has remained elusive, with l…
Faster high-accuracy log-concave sampling via algorithmic warm starts
Jason M. Altschuler, Sinho Chewi
Understanding the complexity of sampling from a strongly log-concave and log-smooth distribution on to high accuracy is a fundamental problem, both from a practi…
Improved Discretization Analysis for Underdamped Langevin Monte Carlo
Matthew Zhang, Sinho Chewi, Mufan Bill Li +2
Underdamped Langevin Monte Carlo (ULMC) is an algorithm used to sample from unnormalized densities by leveraging the momentum of a particle moving in a potential well. We provide a…