12 citations · 15 across the 6 of their papers we have counts for
9 papers
Fast Iterative Combinatorial Auctions via Deep Reinforcement Learning
Luis F. Paul Tosta, Quinn Arnold, Gianluca Brero
Iterative combinatorial auctions are used in spectrum allocation, transportation, and industrial procurement, but rounds are costly. We introduce FastCombo, a deep reinforcement le…
Making a Case for Research Collaboration Between Artificial Intelligence and Operations Research Experts
Radhika Kulkarni, Gianluca Brero, Yu Ding +7
In 2021, INFORMS, ACM SIGAI, and the Computing Community Consortium (CCC) hosted three workshops to explore synergies between Artificial Intelligence (AI) and Operations Research (…
Stackelberg POMDP: Learning to Lead via Reinforcement Learning
Gianluca Brero, Alon Eden, Darshan Chakrabarti +6
Many real-world domains--including e-commerce platform design, security planning, and multi-agent coordination--feature leader-follower problems where one decision-maker commits to…
Learning to Mitigate AI Collusion on Economic Platforms
Gianluca Brero, Nicolas Lepore, Eric Mibuari +1
Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion, where reinforcement learning algorithms learn to set collusive prices in a decentralized m…
Reinforcement Learning of Sequential Price Mechanisms
Gianluca Brero, Alon Eden, Matthias Gerstgrasser +2
We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship a…
iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding
Benjamin Lubin, Manuel Beyeler, Gianluca Brero +1
Preference elicitation is a major challenge in large combinatorial auctions because the bundle space grows exponentially in the number of items. Recent work has used machine learni…
Machine Learning-powered Iterative Combinatorial Auctions
Gianluca Brero, Benjamin Lubin, Sven Seuken
We present a machine learning-powered iterative combinatorial auction (MLCA). The main goal of integrating machine learning (ML) into the auction is to improve preference elicitati…
Fast Iterative Combinatorial Auctions via Bayesian Learning
Gianluca Brero, Sébastien Lahaie, Sven Seuken
Iterative combinatorial auctions (CAs) are often used in multi-billion dollar domains like spectrum auctions, and speed of convergence is one of the crucial factors behind the choi…
A Bayesian Clearing Mechanism for Combinatorial Auctions
Gianluca Brero, Sébastien Lahaie
We cast the problem of combinatorial auction design in a Bayesian framework in order to incorporate prior information into the auction process and minimize the number of rounds to…