This paper studies a house allocation problem in a networked housing mar...
Bayesian persuasion and its derived information design problem has been ...
This paper studies a mechanism design problem over a network, where agen...
This paper explores reward mechanisms for a query incentive network in w...
Most of the work in auction design literature assumes that bidders behav...
We consider designing reward schemes that incentivize agents to create
h...
We consider a variant of the standard Bayesian mechanism, where players
...
The MineRL competition is designed for the development of reinforcement
...
The Borda voting rule is a positional scoring rule for z candidates such...
Modern machine learning models (such as deep neural networks and boostin...
In a multi-party machine learning system, different parties cooperate on...
We consider the principal-agent problem with heterogeneous agents. Previ...
Reinforcement learning algorithms such as the deep deterministic policy
...
In this paper, we design gross product maximization mechanisms which
inc...
In machine learning, it is observed that probabilistic predictions somet...
Click-through rate (CTR) prediction has been one of the most central pro...
We are interested in the setting where a seller sells sequentially arriv...
Model-free reinforcement learning methods such as the Proximal Policy
Op...
We characterize the class of group-strategyproof mechanisms for single
f...
We study a setting of reinforcement learning, where the state transition...
Using AI approaches to automatically design mechanisms has been a centra...
Bike sharing provides an environment-friendly way for traveling and is
b...
We study a generalized contextual-bandits problem, where there is a stat...
In this study, we apply reinforcement learning techniques and propose wh...
We study the problem of allocating impressions to sellers in e-commerce
...
We study the problem of allocating impressions to sellers in e-commerce
...
Recently, shared mobility has been proven to be an effective way to reli...