4 papers
Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
Fatemeh Ghaffari, Siddarth Sitaraman, Xutong Liu +2
Online learning to rank (OLTR) studies how to recommend a short ranked list of items from a large pool and improves future rankings based on user clicks. This setting is commonly m…
Offline Clustering of Preference Learning with Active-data Augmentation
Jingyuan Liu, Fatemeh Ghaffari, Xuchuang Wang +3
Preference learning from pairwise feedback is a widely adopted framework in applications such as reinforcement learning with human feedback and recommendations. In many practical s…
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models
Mengfan Xu, Liren Shan, Fatemeh Ghaffari +3
We study a novel heterogeneous multi-agent multi-armed bandit problem with a cluster structure induced by stochastic block models, influencing not only graph topology, but also rew…
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
Fatemeh Ghaffari, Xuchuang Wang, Jinhang Zuo +1
We study the problem of multi-agent multi-armed bandits with adversarial corruption in a heterogeneous setting, where each agent accesses a subset of arms. The adversary can corrup…