Promit Ghosal

According to our database, Promit Ghosal authored at least 14 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery.

Dominik Meier, Sujai Hiremath, Promit Ghosal, Kyra Gan

CoRR, June, 2025

BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions.

Kingsley Yeon, Promit Ghosal, Mihai Anitescu

CoRR, May, 2025

How Private is Your Attention? Bridging Privacy with In-Context Learning.

Soham Bonnerjee, Zhen Wei Yeon, Anna Asch, Sagnik Nandy, Promit Ghosal

CoRR, April, 2025

LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery.

Sujai Hiremath, Promit Ghosal, Kyra Gan

Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2025

Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent.

Sayan Banerjee, Krishna Balasubramanian, Promit Ghosal

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression.

Xuxing Chen, Krishna Balasubramanian, Promit Ghosal, Bhavya Agrawalla

Trans. Mach. Learn. Res., 2024

LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach.

Sujai Hiremath, Promit Ghosal, Kyra Gan

CoRR, 2024

Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent.

Krishnakumar Balasubramanian, Sayan Banerjee, Promit Ghosal

CoRR, 2024

Hybrid Global Causal Discovery with Local Search.

Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao, Promit Ghosal, Kyra Gan

CoRR, 2024

Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models.

Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao, Promit Ghosal, Kyra Gan

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression.

Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal, Bhavya Agrawalla

CoRR, 2023

Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent.

Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable.

Promit Ghosal, Srinath Mahankali, Yihang Sun

CoRR, 2022

2021

Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections.

Nabarun Deb, Promit Ghosal, Bodhisattva Sen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021