563 citations · 1.2k across the 52 of their papers we have counts for
9 papers · 1 filter
Learning to maximize global influence from local observations
Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
We study a family online influence maximization problems where in a sequence of rounds , a decision maker selects one from a large number of agents with the goal of m…
Multiplayer bandits without observing collision information
Gabor Lugosi, Abbas Mehrabian
We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involve…
Online Influence Maximization with Local Observations
Julia Olkhovskaya, Gergely Neu, Gábor Lugosi
We consider an online influence maximization problem in which a decision maker selects a node among a large number of possibilities and places a piece of information at the node. T…
On the Hardness of Inventory Management with Censored Demand Data
Gábor Lugosi, Mihalis G. Markakis, Gergely Neu
We consider a repeated newsvendor problem where the inventory manager has no prior information about the demand, and can access only censored/sales data. In analogy to multi-armed…
Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi +1
Boltzmann exploration is a classic strategy for sequential decision-making under uncertainty, and is one of the most standard tools in Reinforcement Learning (RL). Despite its wide…
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
Yevgeny Seldin, Gábor Lugosi
We present a new strategy for gap estimation in randomized algorithms for multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and Slivkins (2014). In the stochast…