activity
20082026
most citedByzantine Multiple Access

5 citations · 10 across the 22 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.IT2025

Sequential Adversarial Hypothesis Testing

Eeshan Modak, Mayank Bakshi, Bikash Kumar Dey +1

We study the adversarial binary hypothesis testing problem in the sequential setting. Associated with each hypothesis is a closed, convex set of distributions. Given the hypothesis…

cs.IT2025

Byzantine-Resilient Distributed Computation via Task Replication and Local Computations

Aayush Rajesh, Nikhil Karamchandani, Vinod M. Prabhakaran

We study a distributed computation problem in the presence of Byzantine workers where a central node wishes to solve a task that is divided into independent sub-tasks, each of whic…

cs.LG2025

Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model

Malhar A. Managoli, Vinod M. Prabhakaran, Suhas Diggavi

Federated learning with heterogeneous data and personalization has received significant recent attention. Separately, robustness to corrupted data in the context of federated learn…

cs.IT2025

Byzantine Distributed Function Computation

Hari Krishnan P. Anilkumar, Neha Sangwan, Varun Narayanan +1

We study the distributed function computation problem with users of which at most may be controlled by an adversary and characterize the set of functions of the sources the…

cs.IT2025

Robust Hypothesis Testing with Abstention

Malhar A. Managoli, K. R. Sahasranand, Vinod M. Prabhakaran

We study the binary hypothesis testing problem where an adversary may potentially corrupt a fraction of the samples. The detector is, however, permitted to abstain from making a de…

cs.IT2025

Fractional Subadditivity of Submodular Functions: Equality Conditions and Their Applications

Gunank Jakhar, Gowtham R. Kurri, Suryajith Chillara +1

Submodular functions are known to satisfy various forms of fractional subadditivity. This work investigates the conditions for equality to hold exactly or approximately in the frac…