Promit Ghosal

19 papers A* 4A 1Journal 14
YearRankTypeTitle / Venue / Authors
2026 J jnl
Finite-Particle Rates for Regularized Stein Variational Gradient Descent.
CoRR
Ye He, Krishnakumar Balasubramanian, Sayan Banerjee, Promit Ghosal
2026 J jnl
Statistical Inference for Linear Functionals of Online Least-Squares SGD When t ≳ d
IEEE Trans. Inf. Theory
Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal
2025 J jnl
BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions.
CoRR
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
2025 J jnl
Clustering by Denoising: Latent plug-and-play diffusion for single-cell data.
CoRR
Dominik Meier, Shixing Yu, Sagnik Nandy, Promit Ghosal, Kyra Gan
2025 J jnl
How Private is Your Attention? Bridging Privacy with In-Context Learning.
CoRR
Soham Bonnerjee, Zhen Wei Yeon, Anna Asch, Sagnik Nandy, Promit Ghosal
2025 A* conf
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent.
ICLR
Sayan Banerjee, Krishna Balasubramanian, Promit Ghosal
2025 A conf
LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery.
UAI
Sujai Hiremath, Promit Ghosal, Kyra Gan
2025 J jnl
Statistical Inference for Linear Functionals of Online Least-squares SGD when t ≳ d
CoRR
Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal
2025 J jnl
When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery.
CoRR
Dominik Meier, Sujai Hiremath, Promit Ghosal, Kyra Gan
2024 J jnl
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression.
Trans. Mach. Learn. Res.
Xuxing Chen, Krishna Balasubramanian, Promit Ghosal, Bhavya Agrawalla
2024 J jnl
Hybrid Global Causal Discovery with Local Search.
CoRR
Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao, Promit Ghosal, Kyra Gan
2024 A* conf
Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models.
NeurIPS
Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao, Promit Ghosal, Kyra Gan
2024 J jnl
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent.
CoRR
Krishnakumar Balasubramanian, Sayan Banerjee, Promit Ghosal
2024 J jnl
LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach.
CoRR
Sujai Hiremath, Promit Ghosal, Kyra Gan
2023 J jnl
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression.
CoRR
Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal, Bhavya Agrawalla
2023 A* conf
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent.
NeurIPS
Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
2023 J jnl
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent.
CoRR
Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
2022 J jnl
Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable.
CoRR
Promit Ghosal, Srinath Mahankali, Yihang Sun
2021 A* conf
Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections.
NeurIPS
Nabarun Deb, Promit Ghosal, Bodhisattva Sen