130 citations · 362 across the 14 of their papers we have counts for
16 papers
Digesting the proof of the sharp thin-shell inequality
Yuansi Chen, Boaz Klartag
We present a proof that determines the optimal value of the universal constant in the thin-shell theorem for log-concave distributions in high dimensions. We prove that for any log…
Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
Keru Wu, Yuansi Chen, Wooseok Ha +1
Domain adaptation (DA) is a statistical learning problem that arises when the distribution of the source data used to train a model differs from that of the target data used to eva…
When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?
Yuansi Chen, Khashayar Gatmiry, Minhui Jiang
We analyze the mixing time of Metropolized Hamiltonian Monte Carlo (HMC) with the leapfrog integrator to sample from a distribution on whose log-density is smooth, h…
A Simple Proof of the Mixing of Metropolis-Adjusted Langevin Algorithm under Smoothness and Isoperimetry
Yuansi Chen, Khashayar Gatmiry
We study the mixing time of Metropolis-Adjusted Langevin algorithm (MALA) for sampling a target density on . We assume that the target density satisfies -isoperi…
Hit-and-run mixing via localization schemes
Yuansi Chen, Ronen Eldan
We analyze the hit-and-run algorithm for sampling uniformly from an isotropic convex body in dimensions. We show that the algorithm mixes in time , w…
Localization Schemes: A Framework for Proving Mixing Bounds for Markov Chains
Yuansi Chen, Ronen Eldan
Two recent and seemingly-unrelated techniques for proving mixing bounds for Markov chains are: (i) the framework of Spectral Independence, introduced by Anari, Liu and Oveis Gharan…