research
∙
03/20/2023
A Strategy-proof Mechanism For Networked Housing Markets
This paper studies a house allocation problem in a networked housing mar...
share
research
∙
03/19/2023
Sequential Persuasion Using Limited Experiments
Bayesian persuasion and its derived information design problem has been ...
research
∙
02/16/2023
A Truthful Referral Auction Over Networks
This paper studies a mechanism design problem over a network, where agen...
research
∙
02/13/2023
Collusion-proof And Sybil-proof Reward Mechanisms For Query Incentive Networks
This paper explores reward mechanisms for a query incentive network in w...
research
∙
10/12/2022
Auto-bidding Equilibrium in ROI-Constrained Online Advertising Markets
Most of the work in auction design literature assumes that bidders behav...
research
∙
04/30/2022
Optimal Anonymous Independent Reward Scheme Design
We consider designing reward schemes that incentivize agents to create h...
research
∙
01/26/2022
Characterization of Incentive Compatibility of an Ex-Ante Constrained Player
We consider a variant of the standard Bayesian mechanism, where players ...
research
∙
11/17/2021
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
The MineRL competition is designed for the development of reinforcement ...
research
∙
10/07/2020
New Complexity Results on Coalitional Manipulation of Borda
The Borda voting rule is a positional scoring rule for z candidates such...
research
∙
10/03/2020
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Modern machine learning models (such as deep neural networks and boostin...
research
∙
01/24/2020
Mechanism Design for Multi-Party Machine Learning
In a multi-party machine learning system, different parties cooperate on...
research
∙
11/11/2019
Optimal Common Contract with Heterogeneous Agents
We consider the principal-agent problem with heterogeneous agents. Previ...
research
∙
09/09/2019
Deterministic Value-Policy Gradients
Reinforcement learning algorithms such as the deep deterministic policy ...
research
∙
07/10/2019
Optimal mechanisms with budget for user generated contents
In this paper, we design gross product maximization mechanisms which inc...
research
∙
05/26/2019
Towards reliable and fair probabilistic predictions: field-aware calibration with neural networks
In machine learning, it is observed that probabilistic predictions somet...
research
∙
04/25/2019
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
Click-through rate (CTR) prediction has been one of the most central pro...
research
∙
12/07/2018
Optimal Dynamic Auctions are Virtual Welfare Maximizers
We are interested in the setting where a seller sells sequentially arriv...
research
∙
11/18/2018
Policy Optimization with Model-based Explorations
Model-free reinforcement learning methods such as the Proximal Policy Op...
research
∙
08/20/2018
Group-Strategyproof mechanisms for facility location with Euclidean distance
We characterize the class of group-strategyproof mechanisms for single f...
research
∙
07/10/2018
Generalized deterministic policy gradient algorithms
We study a setting of reinforcement learning, where the state transition...
research
∙
05/09/2018
Computer-aided mechanism design: designing revenue-optimal mechanisms via neural networks
Using AI approaches to automatically design mechanisms has been a centra...
research
∙
02/13/2018
Rebalancing Dockless Bike Sharing Systems
Bike sharing provides an environment-friendly way for traveling and is b...
research
∙
02/12/2018
Policy Gradients for Contextual Bandits
We study a generalized contextual-bandits problem, where there is a stat...
research
∙
11/28/2017
Reinforcement Mechanism Design, with Applications to Dynamic Pricing in Sponsored Search Auctions
In this study, we apply reinforcement learning techniques and propose wh...
research
∙
08/25/2017
Reinforcement Mechanism Design for e-commerce
We study the problem of allocating impressions to sellers in e-commerce ...
research
∙
08/25/2017
A deep reinforcement learning framework for allocating buyer impressions in e-commerce websites
We study the problem of allocating impressions to sellers in e-commerce ...
research
∙
07/06/2017
