5 papers · 1 filter
Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret
Avishek Ghosh
We study fair multi-armed bandits under the Nash Social Welfare (NSW) objective, which measures performance via the geometric mean of accumulated rewards. Existing work defines Nas…
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…
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…
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.…