40 citations · 70 across the 19 of their papers we have counts for
6 papers · 1 filter
Sampling and Integration of Logconcave Functions by Algorithmic Diffusion
Yunbum Kook, Santosh S. Vempala
We study the complexity of sampling, rounding, and integrating arbitrary logconcave functions. Our new approach provides the first complexity improvements in nearly two decades for…
Covariance estimation using Markov chain Monte Carlo
Yunbum Kook, Matthew S. Zhang
We investigate the complexity of covariance matrix estimation for Gibbs distributions based on dependent samples from a Markov chain. We show that when satisfies a Poincaré ine…
Rényi-infinity constrained sampling with membership queries
Yunbum Kook, Matthew S. Zhang
Uniform sampling over a convex body is a fundamental algorithmic problem, yet the convergence in KL or Rényi divergence of most samplers remains poorly understood. In this work, we…
In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
Yunbum Kook, Santosh S. Vempala, Matthew S. Zhang
We present a new random walk for uniformly sampling high-dimensional convex bodies. It achieves state-of-the-art runtime complexity with stronger guarantees on the output than prev…
Sampling from the Mean-Field Stationary Distribution
Yunbum Kook, Matthew S. Zhang, Sinho Chewi +2
We study the complexity of sampling from the stationary distribution of a mean-field SDE, or equivalently, the complexity of minimizing a functional over the space of probability m…
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook +1
Despite the success of the Adam optimizer in practice, the theoretical understanding of its algorithmic components still remains limited. In particular, most existing analyses of A…