Showing cs.LGShow all
3 papers · 1 filter
cs.LG2022
Multi-Player Bandits Robust to Adversarial Collisions
Shivakumar Mahesh, Anshuka Rangi, Haifeng Xu +1
Motivated by cognitive radios, stochastic Multi-Player Multi-Armed Bandits has been extensively studied in recent years. In this setting, each player pulls an arm, and receives a r…
cs.LG2018
Unifying the stochastic and the adversarial Bandits with Knapsack
Anshuka Rangi, Massimo Franceschetti, Long Tran-Thanh
This paper investigates the adversarial Bandits with Knapsack (BwK) online learning problem, where a player repeatedly chooses to perform an action, pays the corresponding cost, an…
cs.LG2018
Online learning with feedback graphs and switching costs
Anshuka Rangi, Massimo Franceschetti
We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actio…