7 citations · 37 across the 25 of their papers we have counts for
10 papers · 1 filter
Optimal Strategies for Federated Learning Maintaining Client Privacy
Uday Bhaskar, Varul Srivastava, Avyukta Manjunatha Vummintala +2
Federated Learning (FL) emerged as a learning method to enable the server to train models over data distributed among various clients. These clients are protective about their data…
FROC: Building Fair ROC from a Trained Classifier
Avyukta Manjunatha Vummintala, Shantanu Das, Sujit Gujar
This paper considers the problem of fair probabilistic binary classification with binary protected groups. The classifier assigns scores, and a practitioner predicts labels using a…
Budgeted Combinatorial Multi-Armed Bandits
Debojit Das, Shweta Jain, Sujit Gujar
We consider a budgeted combinatorial multi-armed bandit setting where, in every round, the algorithm selects a super-arm consisting of one or more arms. The goal is to minimize the…
Federated Learning Meets Fairness and Differential Privacy
Manisha Padala, Sankarshan Damle, Sujit Gujar
Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metri…
Sleeping Combinatorial Bandits
Kumar Abhishek, Ganesh Ghalme, Sujit Gujar +1
In this paper, we study an interesting combination of sleeping and combinatorial stochastic bandits. In the mixed model studied here, at each discrete time instant, an arbitrary \e…
A Multi-Arm Bandit Approach To Subset Selection Under Constraints
Ayush Deva, Kumar Abhishek, Sujit Gujar
We explore the class of problems where a central planner needs to select a subset of agents, each with different quality and cost. The planner wants to maximize its utility while e…