activity
20152024
most citedSupport Recovery in Universal One-bit Compressed Sensing

7 citations · 36 across the 24 of their papers we have counts for

collaborators

28 papers

stat.ML2024

Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD

Aniket Das, Dheeraj Nagaraj, Soumyabrata Pal +2

We consider the problem of high-dimensional heavy-tailed statistical estimation in the streaming setting, which is much harder than the traditional batch setting due to memory cons…

cs.LG2024★ 1 cited

Online Matrix Completion: A Collaborative Approach with Hott Items

Dheeraj Baby, Soumyabrata Pal

We investigate the low rank matrix completion problem in an online setting with users, items, rounds, and an unknown rank- reward matrix ${R}\in \mathbb{R}^{{M…

cs.IR2023

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints

Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam +1

We consider the problem of \emph{blocked} collaborative bandits where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \em…

cs.LG2023★ 1 cited

Optimal Algorithms for Latent Bandits with Cluster Structure

Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam +1

We consider the problem of latent bandits with cluster structure where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \e…

cs.IT2022★ 2 cited

Improved Support Recovery in Universal One-bit Compressed Sensing

Namiko Matsumoto, Arya Mazumdar, Soumyabrata Pal

One-bit compressed sensing (1bCS) is an extremely quantized signal acquisition method that has been proposed and studied rigorously in the past decade. In 1bCS, linear samples of a…

cs.LG2022★ 1 cited

Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components

Soumyabrata Pal, Prateek Varshney, Prateek Jain +5

Personalization of machine learning (ML) predictions for individual users/domains/enterprises is critical for practical recommendation systems. Standard personalization approaches…