56 citations · 238 across the 48 of their papers we have counts for
8 papers · 1 filter
Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
Shixing Yu, Promit Ghosal, Kyra Gan
A critical step for reliable large language models (LLMs) use in healthcare is to attribute predictions to their training data, akin to a medical case study. This requires token-le…
Statistical Inference for Linear Functionals of Online Least-squares SGD when
Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal
Stochastic Gradient Descent (SGD) has become a cornerstone method in modern data science. However, deploying SGD in high-stakes applications necessitates rigorous quantification of…
Clustering by Denoising: Latent plug-and-play diffusion for single-cell data
Dominik Meier, Shixing Yu, Sagnik Nandy +2
Single-cell RNA sequencing (scRNA-seq) enables the study of cellular heterogeneity. Yet, clustering accuracy, and with it downstream analyses based on cell labels, remain challengi…
When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery
Dominik Meier, Sujai Hiremath, Promit Ghosal +1
Distinguishing cause and effect from bivariate observational data is a foundational problem in many disciplines, but challenging without additional assumptions. Additive noise mode…
LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery
Sujai Hiremath, Promit Ghosal, Kyra Gan
Inferring causal relationships from observational data is crucial when experiments are costly or infeasible. Additive noise models (ANMs) enable unique directed acyclic graph (DAG)…
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 +2
Learning the unique directed acyclic graph corresponding to an unknown causal model is a challenging task. Methods based on functional causal models can identify a unique graph, bu…