10 papers
Learning Peer Influence Probabilities with Linear Contextual Bandits
Ahmed Sayeed Faruk, Mohammad Shahverdikondori, Elena Zheleva
In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action. Whether these recommendations are acce…
Active Context Selection Improves Simple Regret in Contextual Bandits
Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash
We study the contextual multi-armed bandit problem with a finite context space (a.k.a. subpopulations), where the learner recommends a best action for each context and is evaluated…
Pure Exploration Beyond Reward Feedback: The Role of Post-Action Context
Mohammad Shahverdikondori, Amir Mohammad Abouei, Alireza Rezaeimoghadam +1
We introduce the problem of best arm identification (BAI) with post-action context, a new BAI problem in a stochastic multi-armed bandit environment and the fixed-confidence settin…
Graph Learning Is Suboptimal in Causal Bandits
Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash
We study regret minimization in causal bandits under causal sufficiency where the underlying causal structure is not known to the agent. Previous work has focused on identifying th…
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference
Fateme Jamshidi, Mohammad Shahverdikondori, Negar Kiyavash
We study multi-armed bandits under network interference, where each unit's reward depends on its own treatment and those of its neighbors in a given graph. This induces an exponent…
Neighborhood-Aware Graph Labeling Problem
Mohammad Shahverdikondori, Sepehr Elahi, Patrick Thiran +1
Motivated by optimization oracles in bandits with network interference, we study the Neighborhood-Aware Graph Labeling (NAGL) problem. Given a graph , a label set of siz…