7 papers
Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons
Kaustubh Shivshankar Shejole, Tanish Agarwal, Arpit Agarwal +1
The problem of learning from pairwise comparisons has been widely studied across many domains such as recommendation systems, social choice, and more recently, fine-tuning large la…
Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs
Panav Shah, Avishek Ghosh
In a federated learning setup for GANs, several adversarial attacks are possible. One such attack is label flipping, in which malicious clients deliberately alter label information…
Optimal Regret for Single Index Bandits
Devdan Dey, Sujoy Bhore, Avishek Ghosh
We study the problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an unknown reward function.…
Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning
Ananya Kunisetty, Avishek Ghosh
We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model. To capture human preferences…
Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners
Drashthi Doshi, Aditya Vema Reddy Kesari, Avishek Ghosh +2
Classical federated learning (FL) assumes that the clients have a limited amount of noisy data with which they voluntarily participate and contribute towards learning a global, mor…
Competing Bandits in Decentralized Contextual Matching Markets
Satush Parikh, Soumya Basu, Avishek Ghosh +1
Sequential learning in a multi-agent resource constrained matching market has received significant interest in the past few years. We study decentralized learning in two-sided matc…