4 citations · 6 across the 14 of their papers we have counts for
14 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…
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…
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…
How Private is Your Attention? Bridging Privacy with In-Context Learning
Soham Bonnerjee, Zhen Wei, Yeon +3
In-context learning (ICL)-the ability of transformer-based models to perform new tasks from examples provided at inference time-has emerged as a hallmark of modern language models.…
Extremal Eigenvalues of Random Kernel Matrices with Polynomial Scaling
David Kogan, Sagnik Nandy, Jiaoyang Huang
We study the spectral norm of random kernel matrices with polynomial scaling, where the number of samples scales polynomially with the data dimension. In this regime, Lu and Yau (2…
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…
PriME: Privacy-aware Membership profile Estimation in networks
Abhinav Chakraborty, Sayak Chatterjee, Sagnik Nandy
This paper presents a novel approach to estimating community membership probabilities for network vertices generated by the Degree Corrected Mixed Membership Stochastic Block Model…
Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph -Model
Sagnik Nandy, Bhaswar B. Bhattacharya
The -model for random graphs is commonly used for representing pairwise interactions in a network with degree heterogeneity. Going beyond pairwise interactions, Stasi…
Bayes optimal learning in high-dimensional linear regression with network side information
Sagnik Nandy, Subhabrata Sen
Supervised learning problems with side information in the form of a network arise frequently in applications in genomics, proteomics and neuroscience. For example, in genetic appli…
Detecting Planted Partition in Sparse Multi-Layer Networks
Anirban Chatterjee, Sagnik Nandy, Ritwik Sadhu
Multilayer networks are used to represent the interdependence between the relational data of individuals interacting with each other via different types of relationships. To study…
Exact Detection Thresholds and Minimax Optimality of Chatterjee's Correlation Coefficient
Arnab Auddy, Nabarun Deb, Sagnik Nandy
Recently, Chatterjee (2021) introduced a new rank-based correlation coefficient which can be used to measure the strength of dependence between two random variables. This coefficie…
Community Detection with Contextual Multilayer Networks
Zongming Ma, Sagnik Nandy
In this paper, we study community detection when we observe sparse networks and a high dimensional covariate matrix, all encoding the same community structure among subject…