8 citations · 13 across the 8 of their papers we have counts for
8 papers
Finite-Particle Rates for Regularized Stein Variational Gradient Descent
Ye He, Krishnakumar Balasubramanian, Sayan Banerjee +1
We derive finite-particle rates for the regularized Stein variational gradient descent (R-SVGD) algorithm introduced by He et al. (2024) that corrects the constant-order bias of th…
A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers
Ye He, Alireza Mousavi-Hosseini, Krishnakumar Balasubramanian +1
We study the complexity of heavy-tailed sampling and present a separation result in terms of obtaining high-accuracy versus low-accuracy guarantees i.e., samplers that require only…
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
Krishnakumar Balasubramanian, Promit Ghosal, Ye He
We derive high-dimensional scaling limits and fluctuations for the online least-squares Stochastic Gradient Descent (SGD) algorithm by taking the properties of the data generating…
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality
Alireza Mousavi-Hosseini, Tyler Farghly, Ye He +2
Langevin diffusions are rapidly convergent under appropriate functional inequality assumptions. Hence, it is natural to expect that with additional smoothness conditions to handle…
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling
Ye He, Tyler Farghly, Krishnakumar Balasubramanian +1
We analyze the complexity of sampling from a class of heavy-tailed distributions by discretizing a natural class of Itô diffusions associated with weighted Poincaré inequalities. B…
Regularized Stein Variational Gradient Flow
Ye He, Krishnakumar Balasubramanian, Bharath K. Sriperumbudur +1
The Stein Variational Gradient Descent (SVGD) algorithm is a deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding…
Heavy-tailed Sampling via Transformed Unadjusted Langevin Algorithm
Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
We analyze the oracle complexity of sampling from polynomially decaying heavy-tailed target densities based on running the Unadjusted Langevin Algorithm on certain transformed vers…
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
The randomized midpoint method, proposed by [SL19], has emerged as an optimal discretization procedure for simulating the continuous time Langevin diffusions. Focusing on the case…