6 citations · 6 across the 7 of their papers we have counts for
17 papers
Strategic investments in multi-stage General Lotto games
Rahul Chandan, Keith Paarporn, Mahnoosh Alizadeh +1
In adversarial interactions, one is often required to make strategic decisions over multiple periods of time, wherein decisions made earlier impact a player's competitive standing…
Collaborative Multi-agent Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh
We study a collaborative multi-agent stochastic linear bandit setting, where agents that form a network communicate locally to minimize their overall regret. In this setting, e…
Multi-Environment Meta-Learning in Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh +2
In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired…
Real-Time Electric Vehicle Smart Charging at Workplaces: A Real-World Case Study
Nathaniel Tucker, Gustavo Cezar, Mahnoosh Alizadeh
We study a real-time smart charging algorithm for electric vehicles (EVs) at a workplace parking lot in order to minimize electricity cost from time-of-use electricity rates and de…
The Division of Assets in Multiagent Systems: A Case Study in Team Blotto Games
Keith Paarporn, Rahul Chandan, Mahnoosh Alizadeh +1
Multi-agent systems are designed to concurrently accomplish a diverse set of tasks at unprecedented scale. Here, the central problems faced by a system operator are to decide (i) h…
Stage-wise Conservative Linear Bandits
Ahmadreza Moradipari, Christos Thrampoulidis, Mahnoosh Alizadeh
We study stage-wise conservative linear stochastic bandits: an instance of bandit optimization, which accounts for (unknown) safety constraints that appear in applications such as…