76 citations · 357 across the 44 of their papers we have counts for
50 papers
One-Shot Private Confidence Regions via Resampling
Shourya Pandey, Purnamrita Sarkar, Po-Ling Loh +1
We propose a simple framework for constructing differentially private confidence regions \textit{in one shot}, i.e., by adding noise only to the final resampling quantile instead o…
Conditional Flow Matching for Transport Between Markov Processes
Syamantak Kumar, Dheeraj Nagaraj, Saptarshi Roy +1
Motivated by sequence-to-sequence transport in the context time-series domain adaptation, we study the problem of transportation between trajectories of Markov processes. Given a l…
Streaming PCA: averaging from a geometric perspective
Tuan Pham, Alessandro Rinaldo, Purnamrita Sarkar
We study principal component analysis (PCA) under memory constraints, a setting that is increasingly important in large-scale data analysis. Our focus is on Oja's algorithm, which…
High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
Syamantak Kumar, Purnamrita Sarkar, Kevin Tian +1
Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the Hamming slice $\mathcal{X}_k^…
Walk fast but be careful: Understanding Parallel Sampling in Masked Diffusion
Vansh Bansal, Cho Cholyeon, Cholyeon Cho +3
In this paper, we use random walks on graphs as a verifiable sandbox for studying parallel sampling strategies in masked diffusion models (MDMs). We train an MDM on random walk sam…
On the Curse of Dimensionality in Private Sparse Covariance Estimation and PCA
Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar +1
We study high-dimensional differentially private (DP) covariance estimation in the operator norm, and principal component analysis (PCA), under -row-column sparsity (-RCS) of…