10 papers
DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing
Sagnik Nandy, Samriddha Lahiry, Pragya Sur +1
Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance. A central challenge is determining the fusion granularity ac…
Transfer Learning in High-Dimensional Clustering: Minimax Thresholds and Applications in Single-Cell Data
Abhinav Chakraborty, Sagnik Nandy
Clustering is a fundamental problem in statistics, with applications across many scientific disciplines. In many modern applications involving clustering, the primary dataset (the…
How does limma-trend work? An empirical partially Bayes perspective
Sagnik Nandy, Wanyi Ling, Nikolaos Ignatiadis
In high-throughput biology, it is common to fit thousands of linear regressions -- one per gene, protein, or other unit -- with very few samples per unit. Limma-trend, one of the m…
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…
Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networks
Bibhabasu Mandal, Sagnik Nandy
In sensitive applications involving relational datasets, protecting information about individual links from adversarial queries is of paramount importance. In many such settings, t…
Multimodal data integration and cross-modal querying via orchestrated approximate message passing
Sagnik Nandy, Zongming Ma
The need for multimodal data integration arises naturally when multiple complementary sets of features are measured on the same sample. Under a dependent multifactor model, we deve…