28 citations · 56 across the 33 of their papers we have counts for
6 papers · 1 filter
Linear programming using diagonal linear networks
Haoyue Wang, Promit Ghosal, Rahul Mazumder
Linear programming has played a crucial role in shaping decision-making, resource allocation, and cost reduction in various domains. In this paper, we investigate the application o…
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression
Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal +1
We conduct a comprehensive investigation into the dynamics of gradient descent using large-order constant step-sizes in the context of quadratic regression models. Within this fram…
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian +1
Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm. Despite its wide usage, understanding the theoretical properties of SV…
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…
Fractal geometry of the PAM in 2D and 3D with white noise potential
Promit Ghosal, Jaeyun Yi
We study the parabolic Anderson model (PAM) \begin{equation} {\partial \over \partial t}u(t,x) =\frac{1}{2}Δu(t,x) + u(t,x)ξ(x), \quad t>0, x\in \mathbb{R}^d, \quad \text{and} \qua…
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal
Stochastic gradient descent (SGD) has emerged as the quintessential method in a data scientist's toolbox. Using SGD for high-stakes applications requires, however, careful quantifi…