4 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
Fisher information lower bounds for sampling
Sinho Chewi, Patrik Gerber, Holden Lee +1
We prove two lower bounds for the complexity of non-log-concave sampling within the framework of Balasubramanian et al. (2022), who introduced the use of Fisher information (FI) bo…
An entropic generalization of Caffarelli's contraction theorem via covariance inequalities
Sinho Chewi, Aram-Alexandre Pooladian
The optimal transport map between the standard Gaussian measure and an -strongly log-concave probability measure is -Lipschitz, as first observed in a celebrated theor…
Improved analysis for a proximal algorithm for sampling
Yongxin Chen, Sinho Chewi, Adil Salim +1
We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain new convergence guarantees under weaker assumptions than strong log-concavity: namely, our results hold for (…
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo
Krishnakumar Balasubramanian, Sinho Chewi, Murat A. Erdogdu +2
For the task of sampling from a density on , where is possibly non-convex but -gradient Lipschitz, we prove that averaged Langevin Monte Ca…